diff --git a/curriculum/3_modeling_and_machine_learning/machine-learning/CV_methods_comparison.ipynb b/curriculum/3_modeling_and_machine_learning/machine-learning/CV_methods_comparison.ipynb
index 610bacfe..b25806ba 100644
--- a/curriculum/3_modeling_and_machine_learning/machine-learning/CV_methods_comparison.ipynb
+++ b/curriculum/3_modeling_and_machine_learning/machine-learning/CV_methods_comparison.ipynb
@@ -2,7 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Testing Cross Validation Strategies\n",
     "Many data science projects aim for generalizable models---models that perform well on new data---such as scoring new credit card applicants or predicting who will win a football game. A common approach to modeling is to split your data, using different splits for different tasks. If you have a lot of data, you might split it into three subsets:\n",
@@ -45,18 +48,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/joewalsh/Library/Python/3.6/lib/python/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
-      "  from pandas.core import datetools\n"
-     ]
-    }
-   ],
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import csv\n",
     "from itertools import product\n",
@@ -64,6 +62,8 @@
     "import pickle\n",
     "import random\n",
     "import warnings\n",
+    "import timeit\n",
+    "import math\n",
     "warnings.filterwarnings(action=\"ignore\", module=\"scipy\", message=\"^internal gelsd\")\n",
     "\n",
     "import numpy as np\n",
@@ -74,7 +74,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "## Regression Example: Predicting US Gross Domestic Product\n",
     "Let's say it's December 31, 2017, and we want to predict next year's nominal US Gross Domestic Product (GDP). We have two intermediate tasks:\n",
@@ -113,7 +116,11 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "GDP_df = pd.read_csv('GDP.csv')"
@@ -122,7 +129,11 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -156,7 +167,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "<!--Look at the dip after 2000. If you randomly assign rows to the train, validate, and test sets, your model might fit post-Great Recession data and validate/test on Great Recession data. Then the model effectively knows GDP drops before it happens, resulting in error rates that are too small for the validation and test sets.  \n",
     "\n",
@@ -179,7 +193,11 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -478,7 +496,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "How many observations do the models have for fitting?"
    ]
@@ -486,7 +507,11 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -505,7 +530,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Let's define the features and target: "
    ]
@@ -513,7 +541,11 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "X_cols = ['year', 'GDP lag 1', 'GDP lag 2', 'Election Year', 'Republican Administration',\n",
@@ -524,7 +556,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "In practice, we can try [hundreds](https://github.com/rayidghani/magicloops/blob/master/mlfunctions.py) or even thousands of model types. To keep things simple, we'll try four here:\n",
     "\n",
@@ -538,7 +573,11 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "clfs = {\n",
@@ -558,7 +597,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "We need to define the range of years for the test sets. Although the data begin in the early 1930s, our test sets begin in the 1940s. Our models use OLS, which requires at least as many rows of data as columns to fit the model. We have 9 predictors (see `X_cols`) plus an intercept, so we need at least 10 rows to fit the model. We get enough rows using 1931 through 1940.\n",
     "\n",
@@ -568,7 +610,11 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "test_begin_year = 1942\n",
@@ -578,7 +624,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "A couple of validation-set rules:\n",
     "\n",
@@ -591,7 +640,11 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "temporal_validation_years = range(test_begin_year-1, test_end_year)\n",
@@ -600,7 +653,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "To visualize the difference between the the two CV techniques, consider an example. Assume it's 1942 and we want to predict GDP for 1943.\n",
     "\n",
@@ -636,7 +692,11 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "def mean_absolute_percentage_error(y_true, y_pred):\n",
@@ -645,7 +705,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "## Standard Cross Validation"
    ]
@@ -653,7 +716,11 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "def standard_etl_fit_evaluate(df, test_year, validation_year, model):\n",
@@ -711,7 +778,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Standard cross validation requires training the models many times over. This can either be done sequentially or with parallelization. Both are demonstrated below and produce the same results. You can change with method is used by changing the boolean `parallel`."
    ]
@@ -719,7 +789,11 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "warnings.filterwarnings(action=\"ignore\", module=\"sklearn\", message=\"^Objective\")"
@@ -728,7 +802,11 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "parallel = True\n",
@@ -759,7 +837,11 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -1060,7 +1142,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "With these results from the cross validation, we can then simulate our process of choosing which model we will use for each of the testing years. To do so, for each fold group (characterized by having the same final cv year) and model type, we average the cv errors from all of the validation years. Then we select the model with the lowest average cv error for the prior years to be our chosen model for that test year. Finally, we compare the performace of the test year's predicted value for GDP with its actual value to get our test error for that year."
    ]
@@ -1068,7 +1153,11 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -1156,7 +1245,11 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# Finding the models with the lowest average cv_error for each year\n",
@@ -1167,7 +1260,11 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -1285,7 +1382,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Now, to estimate the test error of each of our best models, we need to find the error of that model when it is trained on all the years prior to the test year and evaluated on the test year. Eg: the test error for OLS in 1943 is the error produced when OLS is trained on data from 1931 to 1942 and tested on 1943. \n",
     "\n",
@@ -1295,7 +1395,11 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "standard_cv_best_models.rename(columns={\"cv_end\":\"test_year\"}, inplace=True)\n",
@@ -1323,7 +1427,11 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -1508,14 +1616,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "## Temporal Cross Validation"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Temporal CV approaches the problem slightly differently. It uses the same set of validation years, but only trains on the years before the validation year, not the years after. In this way, it eliminates data leakage that can cause overfitting in models trained with Standard CV.\n",
     "    \n",
@@ -1525,7 +1639,11 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "def temporal_etl_fit_evaluate(df, year, model):\n",
@@ -1575,7 +1693,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Once again, we can use either a parallel or a sequential fitting of the models for all of the folds of the cross validation. "
    ]
@@ -1583,7 +1704,11 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "parallel = True\n",
@@ -1610,7 +1735,11 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -1751,7 +1880,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Recall that cv_start and cv_end mark are the inclusive bounds for the validation fold. Thus the training set is all the years in that fold except for the validation year. Temporal CV only ever validates on the last year of the set.\n",
     "\n",
@@ -1761,7 +1893,11 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -1909,6 +2045,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "scrolled": true
    },
    "source": [
@@ -1918,7 +2056,11 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -2068,7 +2210,11 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "temporal_cv_best_models.rename(columns={\"cv_end\":\"test_year\"}, inplace=True)\n",
@@ -2096,7 +2242,11 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -2192,7 +2342,9 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "source": [
     "Now we can compare the models selected and the testing errors generated by our temporal and our standard CV techniques."
@@ -2200,7 +2352,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Results Comparison"
    ]
@@ -2208,7 +2363,11 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -2440,7 +2599,9 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "source": [
     "We can see that in some cases the two methods picked the same model to use as the final predictor model. But even when they chose the same model, they differ in their mean_validation_error. Recall that this mean_validation_error is the average validation error for all of the folds used in the CV. Therefore, if we were picking a model to use to predict a future value, this mean_validation_error would be our best guess for the error of our estimate. We should therefore observe the difference in this predicted error and the true error for test year. Positive values mean the error was overpredicted."
@@ -2449,7 +2610,11 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -2746,7 +2911,11 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -2778,7 +2947,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "From this plot, we can see that standard CV technique appears to predict higher errors than its selected model truly performed. \n",
     "\n",
@@ -2788,7 +2960,11 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -2817,7 +2993,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "We can investigate how this difference in predicted vs actual error changes over the years tested. "
    ]
@@ -2825,7 +3004,11 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -2858,7 +3041,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Temporal CV, mostly because of the rolling window that was employed, tends to have a fairly unbiased prediction of its error for every year. "
    ]
@@ -2866,7 +3052,9 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "source": [
     "### Error By Year, By Model\n",
@@ -2877,7 +3065,11 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -2909,7 +3101,11 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -2941,14 +3137,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "From these we can see that there was not one model that consistently performed the best. In fact, they all show remarkable similarity in their percentage error for each year, except for the prediction of the log transformed values."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### How Often Did Each Method Chose The Best Model?\n",
     "\n",
@@ -2958,7 +3160,11 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "for_ranking = temporal_results_df.pivot(index='validation_year', columns='model', values='validation_error')\n",
@@ -2968,7 +3174,11 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -3099,7 +3309,11 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -3131,7 +3345,11 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -3153,7 +3371,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "From these we can see that temporal tended to pick models that ended up ranking worse in subsequent years for this task."
    ]
@@ -3161,7 +3382,9 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "source": [
     "### Model Regret\n",
@@ -3171,7 +3394,11 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -3269,7 +3496,11 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "temp_vs_std = temp_vs_std.merge(for_ranking.min(axis=1).to_frame().reset_index(), \\\n",
@@ -3280,7 +3511,11 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "temp_vs_std['regret'] = temp_vs_std['test_error'] - temp_vs_std['best_error']"
@@ -3289,7 +3524,11 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -3321,7 +3560,11 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -3352,7 +3595,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "By this metric we can also see that temporal CV has selected models that have been closer to the truly best model for ever year, of the models tested."
    ]
@@ -3360,7 +3606,9 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "source": [
     "# Classification Example: North Carolina Prisoner Recidivism"
@@ -3369,7 +3617,9 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "source": [
     "Perhaps even more ubiquitous than regression is classification-style problems. Here too can using temporal cross validation make a difference in the success of creating a model. \n",
@@ -3380,7 +3630,11 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "data = pd.read_pickle(\"data_cv/recid_data.pickle\")"
@@ -3390,7 +3644,9 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "scrolled": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -3842,71 +4098,111 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
-    "Now we will repeat much of the same process as the regression example. For our error metric, we will consider the precision at the top 50 of predicted cases. Specifically, after the prediction step, we will select those will the highest predicted probablity of being a true label then consider the precision of those."
+    "Now we will repeat much of the same process as the regression example. For our error metric, we will consider the precision at the top 50 of predicted cases. Specifically, after the prediction step, we will the 50 inmates with the highest predicted probablity of being a true label then consider the precision of those."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Setup"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "A significant number of releases begin in 1973 and go through 2016. We will use 1985 though 2012 as our testing years. Recall that test years are years in which I would like to predict our accuracte my model will be. Our validation years are therefore 1981 through 2008. See below for detailed description of the validation systems. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This time, we will compare the temporal cross validation system described above with another method of standard cross validation. For the standard cross validation, we will not treat the years as blocks of observations. Instead, for each validation year, we will perform standard 5 fold cross validation across all the years. This is often how ML practictioners would start out in model comparison. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
-    "A significant number of releases begin in 1973 and go through 2016. So we will use 1974 though 2013 as our testing years. Additionally we will block the release dates by year for purposes of training and testing. Because our error metric is now a grouped metric, we need many cases in a given validation year to test the model, so our validation years will start at 1973 for both standard and cross validation."
+    "There is one very important caveat for creating each test set. Because our target variable looks forward three years, we cannot use training data from less than 3 years before our testing date. For example, if I am trying to create a model on Jan 1, 1987, I can only use training data from before Jan 1, 1984 because, if a person was released in the time between those dates, we would not yet know if they will recitivate within 3 years.\n",
+    "\n",
+    "Thus, here is the exact system that we will use. For temporal CV, we will train each model for each VALIDATION year on only the data from four years before that year. For example, on our first VALIDATION year, 1981, we will train on all the data from or before 1977. Then, we will test each model on 1981. And so forth. Then, to simulate choosing a model in a given TEST year, we will average the errors from the models from only 4 or more years back. For example, in our first TEST year, 1985, the latest data we would have for estimating our model accuracy is for the year 1981. Therefore, we should select a model based on the performance of all our candidate models in 1981. But recall that, to accurately esimate our model error in 1981, the models should only be trained on data from 1977 or before (as that is the only data that would be complete in 1981). In 1986, we can use the errors of the models in 1981 and 1982, and so forth.\n",
+    "\n",
+    "For standard CV, we will use a simpler system, but one that may generate accuracy estimates with significant bias. For each test year, we will take all of the data with full outcomes (4 or more years before, the VALIDATION years) and split it randomly into 5 folds. We will train on 4 folds and test on 1, repeating the process for each fold. We will record the accuracy for each fold and then select a model by averaging these accuracies. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
     "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
     "from sklearn.linear_model import LogisticRegression\n",
     "from sklearn.tree import DecisionTreeClassifier\n",
-    "from sklearn.neighbors import KNeighborsClassifier"
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.model_selection import KFold"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "X_cols = data.columns.difference(['RECITIVATED', 'COMMITMENT_PREFIX', 'COMMITTED_FIRST_NAME', 'COMMITTED_LAST_NAME', 'INMATE_DOC_NUMBER', 'SENTENCE_END', 'SENTENCE_START'])\n",
     "y_cols = 'RECITIVATED'\n",
     "\n",
     "clfs = {\n",
-    "    'rf1': RandomForestClassifier(n_estimators=500, n_jobs=-1, max_depth=30, max_features='log2', min_samples_split=10),\n",
-    "    'rf2': RandomForestClassifier(n_estimators=700, n_jobs=-1, max_depth=20, max_features='log2', min_samples_split=10),\n",
-    "    'extra1': ExtraTreesClassifier(n_estimators=200, n_jobs=-1, criterion='gini', max_depth=50, max_features='log2'),\n",
-    "    'extra2': ExtraTreesClassifier(n_estimators=400, n_jobs=-1, criterion='gini', max_depth=30, max_features='log2'),\n",
-    "    'logistic1': LogisticRegression(penalty='l1', C=0.001),\n",
-    "    'logistic2': LogisticRegression(penalty='l2', C=0.001),\n",
+    "    'rf1': RandomForestClassifier(n_estimators=500, n_jobs=4, max_depth=30, max_features='log2', min_samples_split=10),\n",
+    "    'rf2': RandomForestClassifier(n_estimators=700, n_jobs=4, max_depth=20, max_features='log2', min_samples_split=10),\n",
+    "    'extra1': ExtraTreesClassifier(n_estimators=200, n_jobs=4, criterion='gini', max_depth=50, max_features='log2'),\n",
+    "    'extra2': ExtraTreesClassifier(n_estimators=400, n_jobs=4, criterion='gini', max_depth=30, max_features='log2'),\n",
     "    'decision1': DecisionTreeClassifier(criterion='gini', max_depth=20, max_features='sqrt', min_samples_split=2),\n",
     "    'decision2': DecisionTreeClassifier(criterion='gini', max_depth=50, max_features='sqrt', min_samples_split=2)\n",
     "}\n",
     "\n",
-    "test_begin_year = 1975\n",
-    "test_end_year = 2013\n",
+    "test_begin_year = 1985\n",
+    "test_end_year = 2012\n",
     "test_years = range(test_begin_year, test_end_year+1)\n",
     "\n",
-    "validation_years = range(test_begin_year-1, test_end_year)"
+    "validation_years = range(test_begin_year-4, test_end_year-3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 8,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -3929,64 +4225,143 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Standard Cross Validation"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Pre-generating the folds for ease of parallelization"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "folds = range(0,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "fold_maker = KFold(n_splits=5, shuffle=True, random_state=4312)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "indices = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {
-    "collapsed": true
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
    },
    "outputs": [],
    "source": [
-    "def standard_cv_classification(test_year, validation_year, model):\n",
+    "for year in validation_years:\n",
+    "    complete_data = data[data.SENTENCE_END_YEAR <= year]\n",
     "    \n",
-    "    if validation_year < test_year:\n",
-    "        \n",
-    "        try:\n",
-    "            cv_start = 1950\n",
-    "            cv_end = test_year - 1\n",
-    "        \n",
-    "            # prep validation data\n",
-    "            df_validation = data[data.SENTENCE_END_YEAR == validation_year]\n",
-    "            X_validation = df_validation[X_cols]\n",
-    "            y_validation = df_validation[y_cols]\n",
-    "        \n",
-    "            # prep training data\n",
-    "            df_standard_train = data[(data.SENTENCE_END_YEAR != validation_year) &\\\n",
-    "                                     (data.SENTENCE_END_YEAR < test_year)]\n",
-    "            X_standard_train = df_standard_train[X_cols]\n",
-    "            y_standard_train = np.ravel(df_standard_train[y_cols])\n",
+    "    indices[year] = {'train':{}, 'test':{}}\n",
+    "    \n",
+    "    fold_num = 0\n",
+    "    for train_indices, test_indices in fold_maker.split(complete_data[X_cols]):\n",
+    "        indices[year]['train'][fold_num] = train_indices\n",
+    "        indices[year]['test'][fold_num] = test_indices\n",
     "\n",
-    "            \n",
-    "            # fit the models\n",
-    "            clf = clfs[model]\n",
-    "            clf.fit(X_standard_train, y_standard_train)\n",
-    "            validation_precision = precision_at_abs(y_validation, clf.predict(X_validation))\n",
+    "        fold_num += 1 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def standard_cv_classification(validation_year, model, fold, indices):\n",
+    "    start_time = timeit.default_timer()\n",
     "\n",
-    "            # return the results\n",
-    "            return(cv_start, cv_end, validation_year, model, validation_precision)\n",
-    "        \n",
-    "        except:\n",
-    "            pass"
+    "    complete_data = data[data.SENTENCE_END_YEAR <= validation_year]\n",
+    "\n",
+    "    # prep validation data\n",
+    "    validation_df = complete_data.iloc[indices[validation_year]['test'][fold]]\n",
+    "    validation_X = validation_df[X_cols]\n",
+    "    validation_y = validation_df[y_cols]\n",
+    "\n",
+    "    # prep training data\n",
+    "    training_df = complete_data.iloc[indices[validation_year]['train'][fold]]\n",
+    "    training_X = training_df[X_cols]\n",
+    "    training_y = training_df[y_cols]\n",
+    "\n",
+    "    # fit the model\n",
+    "    clf = clfs[model]\n",
+    "    clf.fit(training_X, training_y)\n",
+    "    index_of_class_1 = [i for i,val in enumerate(clf.classes_) if val == 1][0]\n",
+    "    \n",
+    "    predictions = clf.predict_proba(validation_X)[:, index_of_class_1]\n",
+    "    validation_precision = precision_at_abs(validation_y, predictions)\n",
+    "\n",
+    "    # return the results\n",
+    "    return(validation_year, model, fold, validation_precision, round(timeit.default_timer() - start_time, 2))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "source": [
-    "The above code is how we can generate the models and find their precisions. However, this is a very computationally demanding task, particularly for the latter years of the training set. Therefore we ran an optimized version in parallel on AWS. See `data_cv/get_cv_results.py` for the code. The results are stored in `data_cv/cv_results.csv`."
+    "The above code is how we can generate the models and find their precisions. However, this is a very computationally demanding task, particularly for the latter years of the training set. Therefore we ran an optimized version in parallel on AWS. See `data_cv/get_cv_results_KFold.py` for the code. The results are stored in `data_cv/KFold_cv_results.csv`. Similarly, the results for the temporal CV are stored in `data_cv/temporal_cv_results.csv`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -4009,216 +4384,130 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>cv_start</th>\n",
-       "      <th>cv_end</th>\n",
        "      <th>validation_year</th>\n",
        "      <th>model</th>\n",
+       "      <th>fold</th>\n",
        "      <th>val_precision</th>\n",
+       "      <th>time</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>1973</td>\n",
+       "      <th>196</th>\n",
+       "      <td>1981</td>\n",
        "      <td>decision1</td>\n",
-       "      <td>0.58</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>1.27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>1973</td>\n",
-       "      <td>decision2</td>\n",
-       "      <td>0.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>1973</td>\n",
-       "      <td>extra1</td>\n",
-       "      <td>0.61</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>118</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>1973</td>\n",
-       "      <td>extra2</td>\n",
-       "      <td>0.79</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>78</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>1973</td>\n",
-       "      <td>logistic1</td>\n",
-       "      <td>0.56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>94</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>1973</td>\n",
-       "      <td>logistic2</td>\n",
-       "      <td>0.81</td>\n",
+       "      <td>1981</td>\n",
+       "      <td>decision1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>0.60</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>1973</td>\n",
-       "      <td>rf1</td>\n",
-       "      <td>0.82</td>\n",
+       "      <th>1</th>\n",
+       "      <td>1981</td>\n",
+       "      <td>decision1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.66</td>\n",
+       "      <td>0.59</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>72</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>1973</td>\n",
-       "      <td>rf2</td>\n",
-       "      <td>0.83</td>\n",
+       "      <th>3</th>\n",
+       "      <td>1981</td>\n",
+       "      <td>decision1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>0.61</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>153</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>1974</td>\n",
+       "      <th>4</th>\n",
+       "      <td>1981</td>\n",
        "      <td>decision1</td>\n",
-       "      <td>0.57</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.63</td>\n",
+       "      <td>0.61</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>240</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>1974</td>\n",
+       "      <th>6</th>\n",
+       "      <td>1981</td>\n",
        "      <td>decision2</td>\n",
-       "      <td>0.44</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>148</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>extra1</td>\n",
-       "      <td>0.55</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>0.92</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>225</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>extra2</td>\n",
-       "      <td>0.64</td>\n",
+       "      <th>8</th>\n",
+       "      <td>1981</td>\n",
+       "      <td>decision2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>0.77</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>315</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>logistic1</td>\n",
-       "      <td>0.41</td>\n",
+       "      <th>2</th>\n",
+       "      <td>1981</td>\n",
+       "      <td>decision2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>0.82</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>172</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>logistic2</td>\n",
-       "      <td>0.79</td>\n",
+       "      <th>5</th>\n",
+       "      <td>1981</td>\n",
+       "      <td>decision2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.51</td>\n",
+       "      <td>0.84</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>120</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>1974</td>\n",
-       "      <td>rf1</td>\n",
-       "      <td>0.68</td>\n",
+       "      <th>7</th>\n",
+       "      <td>1981</td>\n",
+       "      <td>decision2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.51</td>\n",
+       "      <td>0.86</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     cv_start  cv_end  validation_year      model  val_precision\n",
-       "88       1950    1974             1973  decision1           0.58\n",
-       "0        1950    1974             1973  decision2           0.43\n",
-       "83       1950    1974             1973     extra1           0.61\n",
-       "118      1950    1974             1973     extra2           0.79\n",
-       "78       1950    1974             1973  logistic1           0.56\n",
-       "94       1950    1974             1973  logistic2           0.81\n",
-       "38       1950    1974             1973        rf1           0.82\n",
-       "72       1950    1974             1973        rf2           0.83\n",
-       "153      1950    1974             1974  decision1           0.57\n",
-       "240      1950    1974             1974  decision2           0.44\n",
-       "148      1950    1974             1974     extra1           0.55\n",
-       "225      1950    1974             1974     extra2           0.64\n",
-       "315      1950    1974             1974  logistic1           0.41\n",
-       "172      1950    1974             1974  logistic2           0.79\n",
-       "120      1950    1974             1974        rf1           0.68"
+       "     validation_year      model  fold  val_precision  time\n",
+       "196             1981  decision1     0           0.67  1.27\n",
+       "0               1981  decision1     1           0.67  0.60\n",
+       "1               1981  decision1     2           0.66  0.59\n",
+       "3               1981  decision1     3           0.64  0.61\n",
+       "4               1981  decision1     4           0.63  0.61\n",
+       "6               1981  decision2     0           0.50  0.92\n",
+       "8               1981  decision2     1           0.56  0.77\n",
+       "2               1981  decision2     2           0.53  0.82\n",
+       "5               1981  decision2     3           0.51  0.84\n",
+       "7               1981  decision2     4           0.51  0.86"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "c_standard_cv_results_df = pd.read_csv('data_cv/cv_results.csv', \\\n",
-    "                                       names=['cv_start', 'cv_end', 'validation_year', 'model', 'val_precision'])\n",
-    "c_standard_cv_results_df.sort_values(['cv_start', 'cv_end', 'validation_year', 'model'], inplace=True)\n",
-    "c_standard_cv_results_df = c_standard_cv_results_df[c_standard_cv_results_df.cv_end != 1973] # Data fully starts in 1973\n",
-    "c_standard_cv_results_df.head(15)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we can repeat the methods used above to select our best model for each year and compare its estimated performance to its actual for the test year. Note two changes: 1) We now want the maximum precision, not the minimum error. 2) Training the model for our final year is now prohibitively expensive, so we will simply disregard that year."
+    "c_standard_cv_results_df = pd.read_csv('data_cv/KFold_cv_results.csv', \\\n",
+    "                                       names=['validation_year', 'model', 'fold', 'val_precision', 'time'])\n",
+    "c_standard_cv_results_df.sort_values(['validation_year', 'model', 'fold'], inplace=True)\n",
+    "c_standard_cv_results_df.head(10)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Grouping by the test year and the model type, what is the average cv_error?\n",
-    "c_grouped_standard_cv_results = c_standard_cv_results_df.groupby(['cv_end', 'model'])['val_precision'].mean().to_frame().reset_index()\n",
-    "c_grouped_standard_cv_results.rename(columns = {\"val_precision\": \"mean_val_precision\"}, inplace=True)\n",
-    "\n",
-    "# Finding the models with the lowest average cv_error for each year\n",
-    "c_standard_cv_best_models = c_grouped_standard_cv_results.loc[c_grouped_standard_cv_results.groupby(['cv_end'])\\\n",
-    "                                                         ['mean_val_precision'].idxmax()]\n",
-    "\n",
-    "# Going from cv_end year to test year\n",
-    "c_standard_cv_best_models.rename(columns={\"cv_end\":\"test_year\"}, inplace=True)\n",
-    "c_standard_cv_best_models[\"test_year\"] = c_standard_cv_best_models[\"test_year\"] + 1 \n",
-    "\n",
-    "c_standard_cv_best_models = c_standard_cv_best_models[c_standard_cv_best_models.test_year != test_end_year]\n",
-    "\n",
-    "test_precisions = []\n",
-    "for index, row in c_standard_cv_best_models.iterrows():\n",
-    "    test_yr = row[\"test_year\"]\n",
-    "    model = row[\"model\"]\n",
-    "    \n",
-    "    test_prec = c_standard_cv_results_df[(c_standard_cv_results_df.cv_end == test_yr) \\\n",
-    "                                      & (c_standard_cv_results_df.validation_year == test_yr)\n",
-    "                                      & (c_standard_cv_results_df.model == model)].iloc[0][\"val_precision\"]\n",
-    "        \n",
-    "    test_precisions.append(test_prec)\n",
-    "\n",
-    "c_standard_cv_best_models[\"test_precisions\"] = test_precisions\n",
-    "c_standard_cv_best_models[\"type\"] = \"standard\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -4241,151 +4530,169 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>test_year</th>\n",
+       "      <th>validation_year</th>\n",
        "      <th>model</th>\n",
-       "      <th>mean_val_precision</th>\n",
-       "      <th>test_precisions</th>\n",
-       "      <th>type</th>\n",
+       "      <th>val_precision</th>\n",
+       "      <th>time</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1975</td>\n",
-       "      <td>logistic2</td>\n",
-       "      <td>0.800000</td>\n",
-       "      <td>0.76</td>\n",
-       "      <td>standard</td>\n",
+       "      <th>15</th>\n",
+       "      <td>1981</td>\n",
+       "      <td>decision1</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>0.65</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>1976</td>\n",
-       "      <td>logistic1</td>\n",
-       "      <td>0.760000</td>\n",
-       "      <td>0.82</td>\n",
-       "      <td>standard</td>\n",
+       "      <th>0</th>\n",
+       "      <td>1981</td>\n",
+       "      <td>decision2</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>2.08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>1977</td>\n",
-       "      <td>logistic1</td>\n",
-       "      <td>0.812500</td>\n",
+       "      <th>16</th>\n",
+       "      <td>1981</td>\n",
+       "      <td>extra1</td>\n",
        "      <td>0.81</td>\n",
-       "      <td>standard</td>\n",
+       "      <td>56.60</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>1978</td>\n",
-       "      <td>logistic1</td>\n",
-       "      <td>0.804000</td>\n",
-       "      <td>0.84</td>\n",
-       "      <td>standard</td>\n",
+       "      <th>13</th>\n",
+       "      <td>1981</td>\n",
+       "      <td>extra2</td>\n",
+       "      <td>0.85</td>\n",
+       "      <td>53.64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>1979</td>\n",
-       "      <td>logistic1</td>\n",
-       "      <td>0.795000</td>\n",
-       "      <td>0.74</td>\n",
-       "      <td>standard</td>\n",
+       "      <th>38</th>\n",
+       "      <td>1981</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>73.20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>1980</td>\n",
-       "      <td>logistic1</td>\n",
-       "      <td>0.771429</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>standard</td>\n",
+       "      <th>14</th>\n",
+       "      <td>1981</td>\n",
+       "      <td>rf2</td>\n",
+       "      <td>0.82</td>\n",
+       "      <td>55.95</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>1981</td>\n",
-       "      <td>extra2</td>\n",
-       "      <td>0.751250</td>\n",
-       "      <td>0.79</td>\n",
-       "      <td>standard</td>\n",
+       "      <th>20</th>\n",
+       "      <td>1982</td>\n",
+       "      <td>decision1</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>0.78</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>63</th>\n",
+       "      <th>1</th>\n",
        "      <td>1982</td>\n",
-       "      <td>rf2</td>\n",
-       "      <td>0.748889</td>\n",
-       "      <td>0.79</td>\n",
-       "      <td>standard</td>\n",
+       "      <td>decision2</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>2.49</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>67</th>\n",
-       "      <td>1983</td>\n",
-       "      <td>extra2</td>\n",
-       "      <td>0.749000</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>standard</td>\n",
+       "      <th>21</th>\n",
+       "      <td>1982</td>\n",
+       "      <td>extra1</td>\n",
+       "      <td>0.85</td>\n",
+       "      <td>68.93</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>75</th>\n",
-       "      <td>1984</td>\n",
+       "      <th>17</th>\n",
+       "      <td>1982</td>\n",
        "      <td>extra2</td>\n",
-       "      <td>0.750909</td>\n",
-       "      <td>0.64</td>\n",
-       "      <td>standard</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>60.97</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "    test_year      model  mean_val_precision  test_precisions      type\n",
-       "5        1975  logistic2            0.800000             0.76  standard\n",
-       "12       1976  logistic1            0.760000             0.82  standard\n",
-       "20       1977  logistic1            0.812500             0.81  standard\n",
-       "28       1978  logistic1            0.804000             0.84  standard\n",
-       "36       1979  logistic1            0.795000             0.74  standard\n",
-       "44       1980  logistic1            0.771429             0.75  standard\n",
-       "51       1981     extra2            0.751250             0.79  standard\n",
-       "63       1982        rf2            0.748889             0.79  standard\n",
-       "67       1983     extra2            0.749000             0.75  standard\n",
-       "75       1984     extra2            0.750909             0.64  standard"
+       "    validation_year      model  val_precision   time\n",
+       "15             1981  decision1           0.53   0.65\n",
+       "0              1981  decision2           0.58   2.08\n",
+       "16             1981     extra1           0.81  56.60\n",
+       "13             1981     extra2           0.85  53.64\n",
+       "38             1981        rf1           0.89  73.20\n",
+       "14             1981        rf2           0.82  55.95\n",
+       "20             1982  decision1           0.53   0.78\n",
+       "1              1982  decision2           0.43   2.49\n",
+       "21             1982     extra1           0.85  68.93\n",
+       "17             1982     extra2           0.83  60.97"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "c_standard_cv_best_models.head(10)"
+    "c_temporal_cv_results_df = pd.read_csv('data_cv/temporal_cv_results.csv', \\\n",
+    "                                       names=['validation_year', 'model', 'val_precision', 'time'])\n",
+    "c_temporal_cv_results_df.sort_values(['validation_year', 'model'], inplace=True)\n",
+    "c_temporal_cv_results_df.head(10)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Temporal Cross Validation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
-    "Note that, as discussed before, all of the folds that would be be trained in temporal CV are also trained in standard, so we can select our `c_temporal_results_df` as a subset of the `c_standard_cv_results_df`. This is also why temporal CV is much more efficient. In cases like this, that translates to lower computation costs or the ability to audition more models."
+    "Next we can repeat the methods used above to select our best model for each year and compare its estimated performance to its actual for the test year. Note two changes: 1) We now want the maximum precision, not the minimum error. 2) Training the model for our final year is now prohibitively expensive, so we will simply disregard that year."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 18,
    "metadata": {
-    "collapsed": true
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
-    "c_temporal_results_df = c_standard_cv_results_df[c_standard_cv_results_df.validation_year == \\\n",
-    "                                                 c_standard_cv_results_df.cv_end]"
+    "# Grouping by the validation year and the model type, what is the average precision across folds?\n",
+    "c_grouped_standard_cv_results = c_standard_cv_results_df.groupby(['validation_year', 'model'])['val_precision'].mean().to_frame().reset_index()\n",
+    "c_grouped_standard_cv_results.rename(columns = {\"val_precision\": \"estimated_precision\"}, inplace=True)\n",
+    "\n",
+    "# Finding the models with the highest average precision for each year\n",
+    "c_standard_cv_best_models = c_grouped_standard_cv_results.loc[c_grouped_standard_cv_results.groupby(['validation_year'])\\\n",
+    "                                                         ['estimated_precision'].idxmax()]\n",
+    "\n",
+    "# Going from validation year to test year: just add 4 years\n",
+    "c_standard_cv_best_models.rename(columns={\"validation_year\":\"test_year\"}, inplace=True)\n",
+    "c_standard_cv_best_models[\"test_year\"] = c_standard_cv_best_models[\"test_year\"] + 4 \n",
+    "\n",
+    "#Now we have the models that we would have selected in any given year. \n",
+    "# Let's retrieve the actual accuracy for that model on that year (in the temporal CV results)\n",
+    "test_precisions = []\n",
+    "for index, row in c_standard_cv_best_models.iterrows():\n",
+    "    test_yr = row[\"test_year\"]\n",
+    "    model = row[\"model\"]\n",
+    "    \n",
+    "    test_prec = c_temporal_cv_results_df[(c_temporal_cv_results_df.validation_year == test_yr) \\\n",
+    "                                       & (c_temporal_cv_results_df.model == model)].iloc[0][\"val_precision\"]\n",
+    "        \n",
+    "    test_precisions.append(test_prec)\n",
+    "\n",
+    "c_standard_cv_best_models[\"actual_precision\"] = test_precisions\n",
+    "c_standard_cv_best_models[\"type\"] = \"standard\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -4408,234 +4715,246 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>cv_start</th>\n",
-       "      <th>cv_end</th>\n",
-       "      <th>validation_year</th>\n",
+       "      <th>test_year</th>\n",
        "      <th>model</th>\n",
-       "      <th>val_precision</th>\n",
+       "      <th>estimated_precision</th>\n",
+       "      <th>actual_precision</th>\n",
+       "      <th>type</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>6256</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>decision1</td>\n",
-       "      <td>0.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6404</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>decision2</td>\n",
-       "      <td>0.63</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6223</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>2011</td>\n",
+       "      <th>2</th>\n",
+       "      <td>1985</td>\n",
        "      <td>extra1</td>\n",
-       "      <td>0.71</td>\n",
+       "      <td>0.916</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>standard</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6367</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>extra2</td>\n",
-       "      <td>0.73</td>\n",
+       "      <th>11</th>\n",
+       "      <td>1986</td>\n",
+       "      <td>rf2</td>\n",
+       "      <td>0.922</td>\n",
+       "      <td>0.93</td>\n",
+       "      <td>standard</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6466</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>logistic1</td>\n",
-       "      <td>0.65</td>\n",
+       "      <th>17</th>\n",
+       "      <td>1987</td>\n",
+       "      <td>rf2</td>\n",
+       "      <td>0.922</td>\n",
+       "      <td>0.94</td>\n",
+       "      <td>standard</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6321</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>logistic2</td>\n",
-       "      <td>0.55</td>\n",
+       "      <th>20</th>\n",
+       "      <td>1988</td>\n",
+       "      <td>extra1</td>\n",
+       "      <td>0.916</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>standard</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6152</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>2011</td>\n",
+       "      <th>28</th>\n",
+       "      <td>1989</td>\n",
        "      <td>rf1</td>\n",
-       "      <td>0.77</td>\n",
+       "      <td>0.904</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>standard</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6444</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>rf2</td>\n",
-       "      <td>0.86</td>\n",
+       "      <th>34</th>\n",
+       "      <td>1990</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>0.930</td>\n",
+       "      <td>0.93</td>\n",
+       "      <td>standard</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6490</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>decision1</td>\n",
-       "      <td>0.63</td>\n",
+       "      <th>40</th>\n",
+       "      <td>1991</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>0.934</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>standard</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6534</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>decision2</td>\n",
-       "      <td>0.56</td>\n",
+       "      <th>46</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>0.954</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>standard</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6472</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>2012</td>\n",
+       "      <th>50</th>\n",
+       "      <td>1993</td>\n",
        "      <td>extra1</td>\n",
-       "      <td>0.70</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6526</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>extra2</td>\n",
-       "      <td>0.76</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6553</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>logistic1</td>\n",
-       "      <td>0.59</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6510</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>logistic2</td>\n",
-       "      <td>0.61</td>\n",
+       "      <td>0.950</td>\n",
+       "      <td>0.85</td>\n",
+       "      <td>standard</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6441</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>2012</td>\n",
+       "      <th>58</th>\n",
+       "      <td>1994</td>\n",
        "      <td>rf1</td>\n",
-       "      <td>0.79</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6548</th>\n",
-       "      <td>1950</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>rf2</td>\n",
-       "      <td>0.77</td>\n",
+       "      <td>0.942</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>standard</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "      cv_start  cv_end  validation_year      model  val_precision\n",
-       "6256      1950    2011             2011  decision1           0.69\n",
-       "6404      1950    2011             2011  decision2           0.63\n",
-       "6223      1950    2011             2011     extra1           0.71\n",
-       "6367      1950    2011             2011     extra2           0.73\n",
-       "6466      1950    2011             2011  logistic1           0.65\n",
-       "6321      1950    2011             2011  logistic2           0.55\n",
-       "6152      1950    2011             2011        rf1           0.77\n",
-       "6444      1950    2011             2011        rf2           0.86\n",
-       "6490      1950    2012             2012  decision1           0.63\n",
-       "6534      1950    2012             2012  decision2           0.56\n",
-       "6472      1950    2012             2012     extra1           0.70\n",
-       "6526      1950    2012             2012     extra2           0.76\n",
-       "6553      1950    2012             2012  logistic1           0.59\n",
-       "6510      1950    2012             2012  logistic2           0.61\n",
-       "6441      1950    2012             2012        rf1           0.79\n",
-       "6548      1950    2012             2012        rf2           0.77"
+       "    test_year   model  estimated_precision  actual_precision      type\n",
+       "2        1985  extra1                0.916              0.89  standard\n",
+       "11       1986     rf2                0.922              0.93  standard\n",
+       "17       1987     rf2                0.922              0.94  standard\n",
+       "20       1988  extra1                0.916              0.90  standard\n",
+       "28       1989     rf1                0.904              0.92  standard\n",
+       "34       1990     rf1                0.930              0.93  standard\n",
+       "40       1991     rf1                0.934              0.99  standard\n",
+       "46       1992     rf1                0.954              0.91  standard\n",
+       "50       1993  extra1                0.950              0.85  standard\n",
+       "58       1994     rf1                0.942              0.90  standard"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "c_temporal_results_df.tail(16)"
+    "c_standard_cv_best_models.head(10)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
-    "When using temporal CV, we often use a rolling average for estimating the precision of models. Ie, instead of simply averaging the precision of all prior years to estimate what our precision will be in the future, we will only average the years going back a set number. This allows the algorithm to be more agile if the model performance begins to change."
+    "## Temporal Cross Validation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "For temporal cross validation, the models are trained the way described above. We also performed these on AWS. The results are stored in `data_cv/temporal_cv_results.csv`. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
-    "rolling_avg_num_years = 5\n",
+    "def temporal_cv_classification(validation_year, model):\n",
+    "    start_time = timeit.default_timer()\n",
+    "\n",
+    "    # prep validation data\n",
+    "    validation_df = data[data.SENTENCE_END_YEAR == validation_year]\n",
+    "    validation_X = validation_df[X_cols]\n",
+    "    validation_y = validation_df[y_cols]\n",
+    "\n",
+    "    # prep training data\n",
+    "    training_df = data[data.SENTENCE_END_YEAR <= validation_year - 4]\n",
+    "    training_X = training_df[X_cols]\n",
+    "    training_y = training_df[y_cols]\n",
+    "\n",
+    "    # fit the model\n",
+    "    clf = clfs[model]\n",
+    "    clf.fit(training_X, training_y)\n",
+    "    index_of_class_1 = [i for i,val in enumerate(clf.classes_) if val == 1][0]\n",
+    "\n",
+    "    predictions = clf.predict_proba(validation_X)[:, index_of_class_1]\n",
+    "    validation_precision = precision_at_abs(validation_y, predictions)\n",
+    "\n",
+    "    # return the results\n",
+    "    return(validation_year, model, validation_precision, round(timeit.default_timer() - start_time, 2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "When using temporal CV, we often use a rolling average for estimating the precision of models. Ie, instead of simply averaging the precision of all prior years to estimate what our precision will be in the future, we will only average the years going back a set number. This allows the algorithm to be more agile if the model performance begins to change. This can be tuned with respect to past data. Here we will use 10 years.\n",
+    "\n",
+    "For example, if we wanted to predict how our model would perform in 1998, the last year that we would have full data for is 1994, so this is our first validation year. Thus our first validation year would be 1989, meaning we average the performances from '89, '90, '91, '92, '93, '94."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "rolling_avg_num_years = 10\n",
     "\n",
     "rows_list = []\n",
     "for validation_yr in validation_years:\n",
     "    for model in clfs:\n",
     "        row_dict = {}\n",
     "        row_dict[\"model\"] = model\n",
-    "        row_dict[\"cv_end\"] = validation_yr\n",
-    "        row_dict[\"mean_val_precision\"] = c_temporal_results_df[\\\n",
-    "                  (c_temporal_results_df.validation_year <= validation_yr) \\\n",
-    "                & (c_temporal_results_df.validation_year >= validation_yr - rolling_avg_num_years) \\\n",
-    "                & (c_temporal_results_df.model == model)][\"val_precision\"].mean()\n",
+    "        row_dict[\"validation_year\"] = validation_yr\n",
+    "        row_dict[\"estimated_precision\"] = c_temporal_cv_results_df[\\\n",
+    "                  (c_temporal_cv_results_df.validation_year <= validation_yr) \\\n",
+    "                & (c_temporal_cv_results_df.validation_year >= validation_yr - (rolling_avg_num_years - 1)) \\\n",
+    "                & (c_temporal_cv_results_df.model == model)][\"val_precision\"].mean()\n",
     "        rows_list.append(row_dict)\n",
     "        \n",
     "c_grouped_temporal_cv_results = pd.DataFrame(rows_list)\n",
     "\n",
-    "# Finding the models with the lowest average cv_error for each year\n",
-    "c_temporal_cv_best_models = c_grouped_temporal_cv_results.loc[c_grouped_temporal_cv_results.groupby(['cv_end'])\\\n",
-    "                                                             ['mean_val_precision'].idxmax()]\n",
-    "\n",
-    "# Going from cv_end year to test year\n",
-    "c_temporal_cv_best_models.rename(columns={\"cv_end\":\"test_year\"}, inplace=True)\n",
-    "c_temporal_cv_best_models[\"test_year\"] = c_temporal_cv_best_models[\"test_year\"] + 1\n",
-    "\n",
-    "c_temporal_cv_best_models = c_temporal_cv_best_models[c_temporal_cv_best_models.test_year != test_end_year]\n",
+    "# Finding the models with the highest estimated precision for each year\n",
+    "c_temporal_cv_best_models = c_grouped_temporal_cv_results.loc[c_grouped_temporal_cv_results.groupby(['validation_year'])\\\n",
+    "                                                             ['estimated_precision'].idxmax()]\n",
     "\n",
+    "# Going from validation year to test year\n",
+    "c_temporal_cv_best_models.rename(columns={\"validation_year\":\"test_year\"}, inplace=True)\n",
+    "c_temporal_cv_best_models[\"test_year\"] = c_temporal_cv_best_models[\"test_year\"] + 4\n",
     "\n",
+    "# Comparing estimated precision to actual precision\n",
     "test_precisions = []\n",
     "for index, row in c_temporal_cv_best_models.iterrows():\n",
     "    test_yr = row[\"test_year\"]\n",
     "    model = row[\"model\"]\n",
     "    \n",
-    "    test_prec = c_temporal_results_df[(c_temporal_results_df.validation_year == test_yr)\n",
-    "                                     & (c_temporal_results_df.model == model)].iloc[0][\"val_precision\"]\n",
+    "    test_prec = c_temporal_cv_results_df[(c_temporal_cv_results_df.validation_year == test_yr)\n",
+    "                                       & (c_temporal_cv_results_df.model == model)].iloc[0][\"val_precision\"]\n",
     "        \n",
     "    test_precisions.append(test_prec)\n",
     "\n",
-    "c_temporal_cv_best_models[\"test_precisions\"] = test_precisions\n",
+    "c_temporal_cv_best_models[\"actual_precision\"] = test_precisions\n",
     "\n",
     "c_temporal_cv_best_models[\"type\"] = \"temporal\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -4658,92 +4977,92 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>test_year</th>\n",
-       "      <th>mean_val_precision</th>\n",
+       "      <th>estimated_precision</th>\n",
        "      <th>model</th>\n",
-       "      <th>test_precisions</th>\n",
+       "      <th>test_year</th>\n",
+       "      <th>actual_precision</th>\n",
        "      <th>type</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1975</td>\n",
-       "      <td>0.790000</td>\n",
-       "      <td>logistic2</td>\n",
-       "      <td>0.76</td>\n",
+       "      <th>0</th>\n",
+       "      <td>0.890000</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>0.86</td>\n",
        "      <td>temporal</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>1976</td>\n",
-       "      <td>0.775000</td>\n",
-       "      <td>logistic2</td>\n",
-       "      <td>0.68</td>\n",
+       "      <th>8</th>\n",
+       "      <td>0.880000</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>1986</td>\n",
+       "      <td>0.93</td>\n",
        "      <td>temporal</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>1977</td>\n",
-       "      <td>0.743333</td>\n",
-       "      <td>logistic2</td>\n",
-       "      <td>0.70</td>\n",
+       "      <th>16</th>\n",
+       "      <td>0.873333</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.97</td>\n",
        "      <td>temporal</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>1978</td>\n",
-       "      <td>0.732500</td>\n",
-       "      <td>logistic2</td>\n",
-       "      <td>0.75</td>\n",
+       "      <th>24</th>\n",
+       "      <td>0.865000</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>1988</td>\n",
+       "      <td>0.97</td>\n",
        "      <td>temporal</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>1979</td>\n",
-       "      <td>0.738000</td>\n",
-       "      <td>logistic1</td>\n",
-       "      <td>0.74</td>\n",
+       "      <th>32</th>\n",
+       "      <td>0.864000</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>1989</td>\n",
+       "      <td>0.92</td>\n",
        "      <td>temporal</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>1980</td>\n",
-       "      <td>0.738333</td>\n",
-       "      <td>logistic1</td>\n",
-       "      <td>0.75</td>\n",
+       "      <th>40</th>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>0.93</td>\n",
        "      <td>temporal</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>1981</td>\n",
-       "      <td>0.795000</td>\n",
-       "      <td>logistic1</td>\n",
-       "      <td>0.76</td>\n",
+       "      <th>48</th>\n",
+       "      <td>0.888571</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>0.99</td>\n",
        "      <td>temporal</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>1982</td>\n",
-       "      <td>0.786667</td>\n",
-       "      <td>logistic1</td>\n",
-       "      <td>0.73</td>\n",
+       "      <th>56</th>\n",
+       "      <td>0.898750</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>0.91</td>\n",
        "      <td>temporal</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>68</th>\n",
-       "      <td>1983</td>\n",
-       "      <td>0.771667</td>\n",
-       "      <td>logistic1</td>\n",
-       "      <td>0.67</td>\n",
+       "      <th>64</th>\n",
+       "      <td>0.901111</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>0.87</td>\n",
        "      <td>temporal</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>75</th>\n",
-       "      <td>1984</td>\n",
-       "      <td>0.766667</td>\n",
-       "      <td>extra2</td>\n",
-       "      <td>0.64</td>\n",
+       "      <th>72</th>\n",
+       "      <td>0.904000</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>1994</td>\n",
+       "      <td>0.90</td>\n",
        "      <td>temporal</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -4751,20 +5070,20 @@
        "</div>"
       ],
       "text/plain": [
-       "    test_year  mean_val_precision      model  test_precisions      type\n",
-       "5        1975            0.790000  logistic2             0.76  temporal\n",
-       "13       1976            0.775000  logistic2             0.68  temporal\n",
-       "21       1977            0.743333  logistic2             0.70  temporal\n",
-       "29       1978            0.732500  logistic2             0.75  temporal\n",
-       "36       1979            0.738000  logistic1             0.74  temporal\n",
-       "44       1980            0.738333  logistic1             0.75  temporal\n",
-       "52       1981            0.795000  logistic1             0.76  temporal\n",
-       "60       1982            0.786667  logistic1             0.73  temporal\n",
-       "68       1983            0.771667  logistic1             0.67  temporal\n",
-       "75       1984            0.766667     extra2             0.64  temporal"
+       "    estimated_precision model  test_year  actual_precision      type\n",
+       "0              0.890000   rf1       1985              0.86  temporal\n",
+       "8              0.880000   rf1       1986              0.93  temporal\n",
+       "16             0.873333   rf1       1987              0.97  temporal\n",
+       "24             0.865000   rf1       1988              0.97  temporal\n",
+       "32             0.864000   rf1       1989              0.92  temporal\n",
+       "40             0.875000   rf1       1990              0.93  temporal\n",
+       "48             0.888571   rf1       1991              0.99  temporal\n",
+       "56             0.898750   rf1       1992              0.91  temporal\n",
+       "64             0.901111   rf1       1993              0.87  temporal\n",
+       "72             0.904000   rf1       1994              0.90  temporal"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4775,15 +5094,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Results Comparison"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
@@ -4806,9 +5132,9 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>mean_val_precision</th>\n",
+       "      <th>actual_precision</th>\n",
+       "      <th>estimated_precision</th>\n",
        "      <th>model</th>\n",
-       "      <th>test_precisions</th>\n",
        "      <th>test_year</th>\n",
        "      <th>type</th>\n",
        "      <th>diff_in_prec</th>\n",
@@ -4817,116 +5143,123 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.800000</td>\n",
-       "      <td>logistic2</td>\n",
-       "      <td>0.76</td>\n",
-       "      <td>1975</td>\n",
+       "      <th>2</th>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.916000</td>\n",
+       "      <td>extra1</td>\n",
+       "      <td>1985</td>\n",
        "      <td>standard</td>\n",
-       "      <td>0.040000</td>\n",
-       "      <td>0.040000</td>\n",
+       "      <td>0.026000</td>\n",
+       "      <td>0.026000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.790000</td>\n",
-       "      <td>logistic2</td>\n",
-       "      <td>0.76</td>\n",
-       "      <td>1975</td>\n",
+       "      <th>0</th>\n",
+       "      <td>0.86</td>\n",
+       "      <td>0.890000</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>1985</td>\n",
        "      <td>temporal</td>\n",
        "      <td>0.030000</td>\n",
        "      <td>0.030000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>0.760000</td>\n",
-       "      <td>logistic1</td>\n",
-       "      <td>0.82</td>\n",
-       "      <td>1976</td>\n",
+       "      <th>11</th>\n",
+       "      <td>0.93</td>\n",
+       "      <td>0.922000</td>\n",
+       "      <td>rf2</td>\n",
+       "      <td>1986</td>\n",
        "      <td>standard</td>\n",
-       "      <td>-0.060000</td>\n",
-       "      <td>0.060000</td>\n",
+       "      <td>-0.008000</td>\n",
+       "      <td>0.008000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>0.775000</td>\n",
-       "      <td>logistic2</td>\n",
-       "      <td>0.68</td>\n",
-       "      <td>1976</td>\n",
+       "      <th>8</th>\n",
+       "      <td>0.93</td>\n",
+       "      <td>0.880000</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>1986</td>\n",
        "      <td>temporal</td>\n",
-       "      <td>0.095000</td>\n",
-       "      <td>0.095000</td>\n",
+       "      <td>-0.050000</td>\n",
+       "      <td>0.050000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>0.812500</td>\n",
-       "      <td>logistic1</td>\n",
-       "      <td>0.81</td>\n",
-       "      <td>1977</td>\n",
+       "      <th>17</th>\n",
+       "      <td>0.94</td>\n",
+       "      <td>0.922000</td>\n",
+       "      <td>rf2</td>\n",
+       "      <td>1987</td>\n",
        "      <td>standard</td>\n",
-       "      <td>0.002500</td>\n",
-       "      <td>0.002500</td>\n",
+       "      <td>-0.018000</td>\n",
+       "      <td>0.018000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>0.743333</td>\n",
-       "      <td>logistic2</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>1977</td>\n",
+       "      <th>16</th>\n",
+       "      <td>0.97</td>\n",
+       "      <td>0.873333</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>1987</td>\n",
        "      <td>temporal</td>\n",
-       "      <td>0.043333</td>\n",
-       "      <td>0.043333</td>\n",
+       "      <td>-0.096667</td>\n",
+       "      <td>0.096667</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "    mean_val_precision      model  test_precisions  test_year      type  \\\n",
-       "5             0.800000  logistic2             0.76       1975  standard   \n",
-       "5             0.790000  logistic2             0.76       1975  temporal   \n",
-       "12            0.760000  logistic1             0.82       1976  standard   \n",
-       "13            0.775000  logistic2             0.68       1976  temporal   \n",
-       "20            0.812500  logistic1             0.81       1977  standard   \n",
-       "21            0.743333  logistic2             0.70       1977  temporal   \n",
+       "    actual_precision  estimated_precision   model  test_year      type  \\\n",
+       "2               0.89             0.916000  extra1       1985  standard   \n",
+       "0               0.86             0.890000     rf1       1985  temporal   \n",
+       "11              0.93             0.922000     rf2       1986  standard   \n",
+       "8               0.93             0.880000     rf1       1986  temporal   \n",
+       "17              0.94             0.922000     rf2       1987  standard   \n",
+       "16              0.97             0.873333     rf1       1987  temporal   \n",
        "\n",
        "    diff_in_prec  abs_diff_in_prec  \n",
-       "5       0.040000          0.040000  \n",
-       "5       0.030000          0.030000  \n",
-       "12     -0.060000          0.060000  \n",
-       "13      0.095000          0.095000  \n",
-       "20      0.002500          0.002500  \n",
-       "21      0.043333          0.043333  "
+       "2       0.026000          0.026000  \n",
+       "0       0.030000          0.030000  \n",
+       "11     -0.008000          0.008000  \n",
+       "8      -0.050000          0.050000  \n",
+       "17     -0.018000          0.018000  \n",
+       "16     -0.096667          0.096667  "
       ]
      },
-     "execution_count": 38,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "c_temp_vs_std = pd.concat([c_standard_cv_best_models, c_temporal_cv_best_models]).sort_values(by=\"test_year\")\n",
-    "c_temp_vs_std[\"diff_in_prec\"] = c_temp_vs_std[\"mean_val_precision\"] - c_temp_vs_std[\"test_precisions\"]\n",
-    "c_temp_vs_std[\"abs_diff_in_prec\"] = abs(c_temp_vs_std[\"mean_val_precision\"] - c_temp_vs_std[\"test_precisions\"])\n",
+    "c_temp_vs_std[\"diff_in_prec\"] = c_temp_vs_std[\"estimated_precision\"] - c_temp_vs_std[\"actual_precision\"]\n",
+    "c_temp_vs_std[\"abs_diff_in_prec\"] = abs(c_temp_vs_std[\"diff_in_prec\"])\n",
     "c_temp_vs_std.head(6)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Let us repeat the post modeling analyses that we conducted for the regression task, beginning with the distribution of the differences in the predicted precision and the actual precisions for the selected model by both methods of CV."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
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AcYRSAAAAGEcoBQAAgHGEUgAAABhHKAUAAIBxhFIAAAAYRygFAACAcS7TDQA4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\nAAAASUVORK5CYII=\n",
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IBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAc\nQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYByn3QWY\nLidvX9DbOhwOufIPyFtcLMuygtrHrYmxQR8fAADAVNyJBQAAgHEIsQAAADAOIRYAAADGIcQCAADA\nOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgA\nAAAYx2l3AcXFxcrJydHXX3+tgoICJSUlqW/fvurWrVu57a+88kq53W45HA5JUtu2bZWdnV2FFQMA\nAMButofYkpIS1a1bV+PHj1fDhg21YcMGPfzww2rYsKFat25d7jZTpkxRampqFVcKAACA6sL26QSR\nkZG67rrr1KhRIzkcDrVt21Zt2rTRhg0b7C4NAAAA1ZTtd2IPV1RUpJ9++klXXHHFEduMGTNGJSUl\natmypbKyspSWllaFFQIAAMBu1SrElpaWaurUqWrZsqXOOuuscts89thjatWqlYqLi/XGG2/ooYce\n0vTp0xUdHe1v4/F45PF4/MthYWGqX79+pdR8aG5uRbatyD7Cw8OD3jYUKlJ7yGqQo1rUYRe7x6Dd\n597u40v2j0GTrwM14TpYHTgcDs4Dap1qE2Ity9L06dP1xx9/aNy4cUe8oJ166qmSpIiICA0YMEAf\nf/yxNmzYoA4dOvjbLFiwQDNmzPAvZ2Vl6c4776yUul35Byq8jwhn8P8MiYmJFT5+RYSi/6HgCouw\nuwTbRES4KrwPpzP48+eKsPfch6L/oRARZl8ddl8HDtg8Bu3uf3XhclWPnwWgqlSLEGtZlnJycrR5\n82Y98sgjioqKOu5tywu7vXv3Dni6QVhYmPLy8kJS6+G8xcVBb+twOBThdKrY55NlWUHto7L6dbwq\n0v9QcYY75Svx2V2GbYqLvUFv63A45HRGyOcrDnoM2j0GKtL/ULF7DNp9HbB7DNrd/+ogJiZGhYWF\ndpcRcvyCgqOpFiH2+eef18aNGzV+/PiAaQGHy83NVXFxsZo2bSqfz6cFCxbI6/WqVatWAe2SkpKU\nlJTkX/Z4PCopKamU2oO96B6+j2D3U1n9Ol6h6H+Fa1Dw568msHsM2n3u7T6+ZP8YrAnXAZOvg9WB\nZVmcB9Q6tofYXbt2adGiRYqIiNDgwYP966+++mr17dtXffv21dixY9WuXTvt2bNHzz33nDwej1wu\nl04++WSNGzdOsbGxNvYAAAAAVc32ENugQQO9/fbbR3x9/vz5/u9PP/10Pffcc1VRFgAAAKox258T\nCwAAAJwoQiwAAACMQ4gFAACS4rFMAAAVLUlEQVSAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAY\nhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDhOuwsAgIp4udcA\nu0uQ0+mUz+ez7fi32nbkP1Xk38DhcMgVESFvcbEsywpqHwNm5gR9/Joi7u7hdpcAVDnuxAIAAMA4\nhFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAA\nABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQ\nCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA\n4zjtLqAquFwuud3uStm3s6Ao6G0dcvy5j3CnLFlB7SMuLi7o44dCRfofKmGOMDmdtWIol6sifQ/F\nGKzN5/4Qu8egydcBxmBoOJ1O28cBUNVqxU++1+uV1+utlH37fL6gt3U4HHKFRchX4pNlBXfxLigo\nCPr4oVCR/oeK0+msFnXYpaJjMCLMVaExWJvP/SF2j0GTrwOhuA4yBv88B3aPg8pQWTegUDMwnQAA\nAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcWrFc2KBmuzlXgOC3tbh\ncMgVESFvcXHQz+iE/XLy9tldQq1XkZ/DUMiaNsXW5+XGDr7VtmOj9uJOLAAAAIxDiAUAAIBxCLEA\nAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAO\nIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAA\nAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHE\nAgAAwDiEWAAAABiHEAsAAADjOO0uIBj79u3TtGnTtHbtWkVFRalXr17KzMy0uywAAABUESND7PPP\nP6/i4mLNmjVLu3bt0oMPPqjU1FR16NDB7tIAAABQBYybTlBUVKQVK1bo+uuvV3R0tJo2bapLLrlE\nS5Yssbs0AAAAVBHjQuz27dtlWZaaNGniX9esWTPl5ubaWBUAAACqknHTCYqKihQdHR2wLiYmRgcO\nHPAvezweeTwe/3JYWJjq169fKfU4HI4Kb1uRfYSHhwe9bShUpPaQ1SBHtajDRKEYg2AMVkQoxmB1\nOPd212D3GLT7/0WonYwLsZGRkQGBVZL279+vqKgo//KCBQs0Y8YM/3JWVpbuvPPOSqlndGJipezX\nFLW9/wCqgeGj7a5AtlcwbITdFQBVzrgQm5KSIknKzc1VWlqaJGnz5s3+7yWpd+/e6tatm385LCxM\neXl5VVvocQgPD1d8fLzy8/NVUlJidznGiomJUWFhod1lGIkxGBqMweAxBkOjpo7BRG6U4CiMC7GR\nkZHq2rWr5syZo3vvvVe7d+/W4sWLdffdd/vbJCUlKSkpyb/s8Xiq9cWxpKSkWtdX3VmWxfmrIMZg\nxTAGK44xWDGMQdRGxoVYSbrlllv07LPPKisrS1FRUerduzeP1wIAAKhFjAyxsbGxGjlypN1lAAAA\nwCbGPWILAAAAIMQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4h\nFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4zgsy7LsLqK28ng8WrBggXr37q2kpCS7y0EtxBiE3RiD\nAILFnVgbeTwezZgxQx6Px+5SUEsxBmE3xiCAYBFiAQAAYBxCLAAAAIwTnp2dnW13EbVZVFSUOnbs\nqOjoaLtLQS3FGITdGIMAgsEfdgEAAMA4TCcAAACAcQixAAAAMI7T7gJqo5dfflnvv/++fD6funbt\nqltvvVURERFHbPvFF1/ol19+Ua9evTRo0KAqrhY11b59+zRt2jStXbtWUVFR6tWrlzIzM+0uCzXQ\n8Y614uJiTZ48WT/99JN27dqlsWPHqkOHDjZUDMAE3ImtYosXL9by5cs1adIk/fOf/9S2bds0d+7c\nI7ZPTk5WVlaWzj777CqsErXB888/r+LiYs2aNUvZ2dl6/fXX9T//8z92l4Ua6ETGWps2bXTvvffy\nwQcAjokQW8WWLl2qzMxMNWrUSPHx8br22mv14YcfHrH9RRddpA4dOvBXuwipoqIirVixQtdff72i\no6PVtGlTXXLJJVqyZIndpaGGOZGxFhERoczMTLVr105hYfzvCcDRcZWoYrm5uWrevLl/uXnz5tq7\nd6/y8vJsrAq1zfbt22VZlpo0aeJf16xZM+Xm5tpYFWoixhqAykKIrWJFRUWKiYnxLx/6/sCBA3aV\nhFqoqKiozN39mJgYxiFCjrEGoLLwh10h9Pjjj+vzzz8/4utvv/22IiMjVVhY6F+3f/9+SX8+7Buo\nKpGRkWVCxP79+xmHCDnGGoDKQogNoZEjRx6zTVpamjZv3qy2bdtKkn7++WfVqVNHiYmJlV0e4JeS\nkiLpz+ktaWlpkqTNmzf7vwdChbEGoLIwnaCKXXTRRXrrrbf022+/qaCgQK+++qouuuiiI7b3+Xzy\ner0qLS1VaWmpvF6vSkpKqrBi1ESRkZHq2rWr5syZo/3792vr1q1avHix0tPT7S4NNcyJjrXi4mJ5\nvV5ZlqWSkhL/9Q8ADsfHzlYxy7I0d+5cvffeeyopKVGXLl102223+Z8Tm52drbZt26pv376SpKlT\np+qjjz4K2Me1116r/v37V3ntqFn27dunZ5991v/szquuuornxKJSHG2s9e3bV2PHjlW7du0kSTfe\neKN27doVsP2jjz6q0047rcrrBlC9EWIBAABgHKYTAAAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIs\nAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCh8nOzpbD4Sj36/HHH7e7vABZWVk69dRTQ7Kv7t27\n+/sZFhamtLQ09e/fX1u3bg3J/o/HsmXL5HA49OWXXx73Ng6HQ5MmTarEqgL99Twd/rVq1arj2seW\nLVuUnZ2tHTt2BKwPpv8VNXv2bM2bN6/KjgcAoeK0uwCgOoqKiirzcb+SlJaWZkM1R/bggw+qsLAw\nZPvr2rWrJk2apJKSEn377bcaM2aMVq9erW+++UbR0dEhO86RtG/fXitXrlSbNm2Oe5uVK1eqSZMm\nlVhVWYfO0+GO9xeKLVu2aNy4cbr88svVuHFj//pg+l9Rs2fPVmxsLB9lDcA4hFigHGFhYTrnnHNO\naJsDBw4oKiqqzHrLsuT1euV2u4Oq5eDBg4qIiFBYWNk3Tlq0aBHUPo8kISHB3++uXbsqJiZGAwcO\n1KJFi3T11VeXaV/Rvh0uPj7+hM/7ibYPhb+ep1AKpv8AUFsxnQAI0qHpBSNGjFCjRo3UoEEDSf//\nFv+iRYt0xhlnyO12a+HChZKkrVu36uqrr1adOnUUExOjHj166Ntvvw3Yb9OmTXXnnXfqiSeeUJMm\nTRQVFaU//vij3BoOn04we/ZsORwOrVu3TpdddpliYmLUsmVLvfTSS0H1sWPHjpKkzZs3H7Nve/bs\n0e23367k5GS53W516NBBixcvLrPPd999V127dlV0dLQSExPVvXt3rVu3TlL5b6fPnDlT7dq1U1RU\nlOrVq6fzzjtPa9as8b9e3nSC559/Xq1atZLb7VbTpk01fvx4lZaWVtp5OlxxcbGGDx+utLQ0ud1u\nJScn64orrtDevXu1bNkyXXDBBZKkTp06+aciHKn/DodDEydO1OjRo9WgQQMlJCTo/vvvl2VZ+vDD\nD3XmmWcqNjZWF110kX755ZeAOkaOHKnTTjtNsbGxSklJUb9+/fTrr7/6X+/evbuWL1+ud999119H\ndna2//V3331XnTt3VlRUlOrXr6/bbrstpHf+AaAiuBMLHIHP5yuzzukM/JF5+umndc455+iFF14I\naL9jxw4NHTpUY8aMUVpamtLS0lRQUKDu3bsrLCxMOTk5ioyM1KOPPqq//e1v+uabb3TSSSf5t1+w\nYIFatmypp59+WuHh4YqJiTmh2q+77jrddNNNGjZsmGbMmKGsrCx16tTphN+mPhRe//qWd3l983q9\nSk9P186dO/Xoo48qJSVFL7/8sjIyMrR27VqddtppkqR///vf6tevnzIzMzVv3jy5XC6tWLFC27dv\n11lnnVXm+J988omGDBmi++67Tz179tT+/fu1evVq7dmz54g1P/PMMxo6dKjuuusuXX755fr888+V\nnZ2tPXv2lAm7wZ4ny7LKjA+Hw6Hw8HBJ0oQJE5STk6OJEyeqXbt28ng8Wrx4sQ4ePKj27dtr2rRp\nuuOOOzRr1iy1bt36qMeSpGeffVbdu3fXnDlz9MUXX2js2LEqKSnRkiVLNHr0aLlcLg0dOlRDhgwJ\n+MVh165deuCBB9S4cWPt3r1bkydPVrdu3fSf//xHTqdT06dP14ABAxQdHe0/N6mpqZKk119/Xddc\nc41uuOEGjRs3Tr/++qtGjhypvLw8vfrqq8esGQAqnQUgwNixYy1J5X59+umn/naSrLZt21qlpaUB\n2w8aNMiSZK1atSpg/dNPP205HA7rP//5j3/d77//bsXExFjDhg3zr2vSpIlVr149a9++fcesddCg\nQVa7du38y7NmzbIkWdOmTfOv27dvnxUdHW098sgjR91Xt27drJ49e1rFxcXWwYMHrS+//NJq27at\nlZCQYP32229H7dvMmTMtp9NprV+/PmB9586drT59+liWZVmlpaVWamqq1aNHjyPW8PHHH1uSrDVr\n1liWZVlPPvmkVbdu3aPWLcl68sknLcuyLJ/PZyUlJVnXXnttQJtRo0ZZLpfL8ng8lmVV/DyVNzbC\nw8P9bTIyMqyrrrrquPt5tPWSrLPPPjugXYcOHcqMpWeeecaSZOXl5ZV7TJ/PZ23bts2SZH3wwQcB\n/cnIyAhoW1paajVp0sTq169fwPr33nvPcjgc1nfffXfEvgFAVWE6AVCOqKgorVmzpszXmWeeGdDu\nsssu878V/Ff16tVT586dA9Z9+umnOvXUUwPu8tWtW1fp6en67LPPAtp27979hO++/tUll1zi/z4m\nJkZNmjTRtm3bjrndokWLFBERIbfbrY4dO8rn8+mNN95Qw4YN/W3K69vixYt12mmn6ZRTTpHP5/N/\npaen+9/637hxo7Zt26bBgwcfdz/at2+vP/74Q1lZWVqyZIn2799/1Pbff/+9PB6P+vTpE7D+mmuu\nkdfr1erVqwPWB3ueDk1p+OvXF198EVD3okWLlJ2drTVr1gRMZQhGenp6wPIpp5yixo0bB4ylU045\nRZIC6n/vvffUpUsX1alTR06n03+X9Ycffjjq8X744Qdt3bpVffv2Dfj37Natm8LCwqr06QkAcCRM\nJwDKERYW5p8PejR/DXfHWp+Xl1fu+oYNG+q77747rv0er4SEhIBll8uloqKiY2533nnnacqUKQoP\nD1dKSop/nu+xavN4PFq3bp0iIiLKvHboLfbff/9dUuDUhGO58MILNWfOHD399NPq0aOHIiMjdfXV\nV2vq1KmqW7dumfZ5eXnl1nho+fC5xcGepzp16hx1fIwePVphYWF68cUXNW7cONWvX1933HGHHnro\noXJ/6TmW8uosb50kf/1r1qzRlVdeqczMTI0cOVINGjSQw+HQOeecc8w+ejweSVKvXr3Kff3wubcA\nYAdCLFABRwok5a2vW7euNm7cWGb9zp07ywSyYIJOKBwrnElH7tvpp5+uF1544Yjb1atXT5LKPBv1\nWAYMGKABAwbI4/Horbfe0r333quIiIhyj3XoPO7atStg/c6dOwNer2xut1vZ2dnKzs7WTz/9pJkz\nZyo7O1vNmzfX9ddfXyU1vPnmm6pTp47mz5/vf7LF8T7z99B5evbZZ8vcdZdO7BcRAKgsTCcAqsh5\n552nb7/9NiDI5uXlaenSpTrvvPNsrKziLr74Yv38889q3LixOnbsWOZLklq1aqXU1FTNmjUrqGMk\nJSVpyJAhSk9P14YNG8pt06pVK9WvX1+vvfZawPr58+fL5XLp7LPPDurYFXHyySfrscceU926df11\nH37XtDIcOHBAERERAb90zJ07t0y78u4+t27dWqmpqfr555/L/fckxAKoDrgTC5SjtLS03E9fatCg\ngZo3bx7UPm+44QZNmTJFGRkZGj9+vP/pBE6nU/fcc09FS7bVwIED9fzzz6t79+667777dMopp2jP\nnj1at26dvF6vJkyY4H8UVr9+/dS7d28NHDhQbrdbK1euVKdOnXT55ZeX2e/YsWP1+++/q3v37mrQ\noIG+/fZbvf/++xo2bFi5dYSHh+vBBx/U0KFD1aBBA/Xs2VOrVq3SxIkTdc899/jvBlfUnj17yh0f\nJ598spKSkvRf//Vf6tChg8466yzFxMRo4cKFysvL04UXXijpz/mr4eHhmjlzppxOp5xO53FNXzkR\n6enpmjp1qu666y716tVLK1eu1Jw5c8q0a9OmjV588UUtXLhQycnJaty4sRo3bqynnnpK/fv3V2Fh\noTIyMhQTE6OtW7fq3Xff1WOPPeafgwsAdiHEAuU4cOCAzj333DLrhwwZon/9619B7TMuLk7Lli3T\nsGHDdPPNN6ukpERdu3bVJ598EvB4LRO53W599NFHys7O1qOPPqpff/1VSUlJOuuss3T77bf7211z\nzTWKjo7Wo48+qmuvvVaRkZFq3779EededurUSVOnTtX8+fOVn5+v1NRUDR8+XGPGjDliLXfddZci\nIiL01FNPafr06UpOTlZ2drYeeOCBkPV3xYoV5Y6POXPmaMCAAeratavmz5+vyZMny+fzqVWrVpo7\nd64uvvhiSX/eVZ42bZqeeOIJzZkzRz6fT5Zlhaw+SerZs6cmTpyoZ555RrNmzVLXrl31zjvvlAmf\n999/v3766ScNHDhQe/bs0dixY5Wdna0+ffooISFBjz76qF5++WVJfz7D+NJLL63wnG0ACAWHFeor\nJwAAAFDJmBMLAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAA\nABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHH+F9J3XSlEDwDGAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11b478080>"
+       "<matplotlib.figure.Figure at 0x11c78e1d0>"
       ]
      },
      "metadata": {},
@@ -4935,10 +5268,10 @@
     {
      "data": {
       "text/plain": [
-       "<ggplot: (296855679)>"
+       "<ggplot: (-9223372036556342696)>"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4952,7 +5285,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "These results indicate that, for this classification task, standard CV does appear to have a slightly biased prediction of precision. Specifically, it appears to be predicting precisions slightly lower than the actual precision for a given year. On the other hand, the predictions of temporal CV are more closely centered around 0 than those of temporal CV, but spread slightly wider.\n",
     "\n",
@@ -4961,14 +5297,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
-   "metadata": {},
+   "execution_count": 70,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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izKuTfD7fgMC2u7sb/jJ3y/f38MMP49577y18vHLlSnzjG9+o6phD8UzCWfWR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\n",
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C5XROcwtu5ykKuGLpg1eJ1xsB28GqnvuYcdp2MfG9/0SPj2EVFa/B6oQQG9Wy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\ndomIiIjIsljsEhEREZFl/X8fFZYQpYtBxQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117de6f60>"
+       "<matplotlib.figure.Figure at 0x11c046940>"
       ]
      },
      "metadata": {},
@@ -4977,10 +5317,10 @@
     {
      "data": {
       "text/plain": [
-       "<ggplot: (294313662)>"
+       "<ggplot: (297853974)>"
       ]
      },
-     "execution_count": 209,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4989,64 +5329,37 @@
     "(ggplot(c_temp_vs_std, aes(x='test_year', y='diff_in_prec', colour='type')) +\n",
     "    geom_line() +\n",
     "    xlab('Year') +\n",
-    "    ylab('Error in Precision Estimate')\n",
+    "    ylab('Error in Precision Estimate') + \n",
+    "    geom_hline(yintercept=0, linetype=\"dashed\")\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This shows us that both methods had fairly unbiased estimates for precision before 2000, at which point both began to predict lower precision than was actually achieved. However, temporal's predictions were generally lower. This is likely because both CV strategies started selecting the model `rf2`, which may have had worse performace in early years and better in later years (recall that the predicted precision for a given year is the average of the precision for all folds prior).\n",
-    "\n",
-    "We can evaluated this hypothesis by looking at the performance of the models across years (4 models at a time)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 210,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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xKjG6upINv42N2C7r2igL13ba9bhxY4p/1NdDVyw4pT1zOcc970nR1dVJZ9rw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WBBW+rHHM7dquc0h8b1Xie/CkQ5OkcSl7dGxl0pzzkcNmHZFS8FAVm7KSw0IhiY0eGgI7WLnbGH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\npUHbRcy+5M3mKpB0Ou35v1xu6YJTiXeUJDn4y7/8S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lapiSAScXxKkIx73elg0Hawrjk3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\nAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1187e4748>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<ggplot: (294016730)>"
-      ]
-     },
-     "execution_count": 210,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
-    "set1 = ['decision1', 'decision2', 'rf1', 'rf2']\n",
-    "set2 = ['extra1', 'extra2', 'logistic1', 'logistic2']\n",
+    "This chart shows that, in the first decade, the temporal estimates of precision were substantially lower than the actual performance for that year. Standard CV, on the other hand, started off relatively unbiased. Then, around the mid 1990's, both methods start estimated much higher precision than was actually observed. This is likely due to a shift in the underlying distribution of how many individuals recidivated during this time. As we can see, after that change, temporal CV more quickly responded to the change and decreased back towards 0.\n",
     "\n",
-    "(ggplot(c_temporal_results_df[c_temporal_results_df.model.isin(set1)], aes(x=\"validation_year\")) +\n",
-    "    geom_line(aes(y=\"val_precision\", color=\"model\"))\n",
-    ")"
+    "We can evaluated this hypothesis by looking at the performance of the models across years."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
-   "metadata": {},
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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fuSr8PsKX3IlZiBCCuOwjrYaXfcxquyshxHSmNRRe1j5sEaDN2sS4d3Z2r9Z1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\nCwsAiHpNxOrohHJ5ar9LtT9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VLFVa9KQnEvipJOVwBJXJkPQVCgiUimT86Y4NO7TPsOtx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wYA6M0tmJdcRmqCrdJEvHWnba8AzjMNbq7x8r2xFF0lZALB8WKsgX057CyMnaz8t5yWcXI42eW8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KSZLEu+++axHrxIkTrfoaMWJEp3F15rnnnrPow2AwCC8vL7FhwwYhRMfvTqfTiYiICIv3HT9+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\naCUiIiIixWPRSkRERESK9/8BOTwMzvILiAkAAAAASUVORK5CYII=\n",
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EkMMyqqSSJWfxqjWbkfpQQlLz5RyVfBV9ZAh3vY01K4Gcp6B0aviZPlo/8r5y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AIHDa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\niXrjAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118195908>"
+       "<matplotlib.figure.Figure at 0x11baa70f0>"
       ]
      },
      "metadata": {},
@@ -5055,39 +5368,47 @@
     {
      "data": {
       "text/plain": [
-       "<ggplot: (-9223372036561908059)>"
+       "<ggplot: (297826077)>"
       ]
      },
-     "execution_count": 211,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "(ggplot(c_temporal_results_df[c_temporal_results_df.model.isin(set2)], aes(x=\"validation_year\")) +\n",
+    "(ggplot(c_temporal_cv_results_df, aes(x=\"validation_year\")) +\n",
     "    geom_line(aes(y=\"val_precision\", color=\"model\"))\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
-    "From these plots, we can see that model `rf2` does indeed have a large improvement in precision after 2000. But even more significant is the large drop in precision of all models in 1995. This sudden change is what is responsible for the poor precision predictions in the years after 1995 by the temporal CV. See the graph below."
+    "Indeed, as speculated, all the models suddenly drop in precision around the year 1995. This sudden change is what is responsible for the poor precision predictions in the years after 1995 by the CV methods. \n",
+    "\n",
+    "For example, let us look at a single model: rf1. Below, we will blot the actual performance of the model every year in black, the Standard CV estimated performance in blue, and the Temporal CV estimated performance in red. See the graph below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 79,
    "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Dh2LIkCE297TJzc1FWloa5k2bBq9nn0X9o49CN3p0u6+TyWS4dOkS1Bs2gKmrg+fLL1t1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\nkk9kbUxsiOzAvn37UFVVhT179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v7LXb4YUXAvjll8LtHY4fd3O9vhAOksBHeAxn83usVlixwofhw/MU2Z3Wz8+P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\nAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11c5a9588>"
+       "<matplotlib.figure.Figure at 0x11bc8c358>"
       ]
      },
      "metadata": {},
@@ -5096,45 +5417,58 @@
     {
      "data": {
       "text/plain": [
-       "<ggplot: (-9223372036556515952)>"
+       "<ggplot: (297690432)>"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "model = 'rf1'\n",
     "(\n",
     "ggplot() + \n",
-    "  #geom_point(c_standard_cv_results_df[c_standard_cv_results_df.model == 'rf2'], aes(x='cv_end', y='val_precision'), color='grey') +\n",
-    "  #geom_point(c_temporal_results_df[c_temporal_results_df.model == 'rf2'], aes(x='cv_end', y='val_precision'), color='black') +\n",
-    "  geom_line(c_grouped_standard_cv_results[c_grouped_standard_cv_results.model == 'rf2'], aes(x=\"cv_end\", y='mean_val_precision'), color = \"blue\") +\n",
-    "  geom_line(c_grouped_temporal_cv_results[c_grouped_temporal_cv_results.model == 'rf2'], aes(x=\"cv_end\", y='mean_val_precision'), color = \"red\") +\n",
-    "  geom_line(c_temporal_results_df[c_temporal_results_df.model == 'rf2'], aes(x=\"cv_end\", y='val_precision'), color = \"black\") +\n",
-    "  xlab('Final Year in CV Fold') +\n",
-    "  ylab('Validation Fold Precision')\n",
-    ")\n"
+    "  geom_line(c_grouped_standard_cv_results[c_grouped_standard_cv_results.model == model], aes(x=\"validation_year\", y='estimated_precision'), color = \"blue\") +\n",
+    "  geom_line(c_grouped_temporal_cv_results[c_grouped_temporal_cv_results.model == model], aes(x=\"validation_year\", y='estimated_precision'), color = \"red\") +\n",
+    "  geom_line(c_temporal_cv_results_df[c_temporal_cv_results_df.model == model], aes(x=\"validation_year\", y='val_precision'), color = \"black\") +\n",
+    "  xlab('Validation Year') +\n",
+    "  ylab('Validation Precision')\n",
+    ")\n",
+    "\n",
+    "# Red: Temporal CV precision estimate\n",
+    "# Blue: Standard CV precision estimate\n",
+    "# Black: Actual model performance"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
-    "Here the X-axis is the same as `test_year - 1`. The red line is the precision estimated by temporal CV, the blue line is the precision estimated by standard CV. The black is the actual performace."
+    "Here, we can see that Standard CV is not estimating the performance with much accuracy, whereas Temporal appears much better. This pattern repeats in all the other models as well."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Investigating the dip in precision in 1995"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
-   "metadata": {},
+   "execution_count": 80,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# Finding the recidivism percentage and number of released prisioners by year.\n",
@@ -5145,8 +5479,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
-   "metadata": {},
+   "execution_count": 81,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# Finding the number of prisoners inducted per year\n",
@@ -5164,23 +5502,29 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "First, we should look at the recidivism percentages for each year to see if there was a significant change around 1995. In the same graph we will plot the precisions of the models selected by both CV strategies."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": 82,
    "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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TM3p76M4rQALKJhiJheFWW88MRTBNE0mSsF5+BZHnfo35vjumJ1hhxnkrbrAs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\nL/sAAAAASUVORK5CYII=\n",
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fpmOVKQXiDLNycymvJ2cyuPEAVxf3OemHZbvdzmWDShhfV8e7VV6eTc2l5/rNTOuUQvfc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\nkkLnJ8FwFLVYth+BAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118195cf8>"
+       "<matplotlib.figure.Figure at 0x11c75cc50>"
       ]
      },
      "metadata": {},
@@ -5189,10 +5533,10 @@
     {
      "data": {
       "text/plain": [
-       "<ggplot: (-9223372036577798908)>"
+       "<ggplot: (297575025)>"
       ]
      },
-     "execution_count": 215,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5201,7 +5545,7 @@
     "warnings.filterwarnings(action=\"ignore\", module=\"mizani\", message=\"^overflow\")\n",
     "\n",
     "(ggplot() + \n",
-    " geom_line(c_temp_vs_std, aes(x=\"test_year\", y=\"test_precisions\", color=\"type\")) +\n",
+    " geom_line(c_temp_vs_std, aes(x=\"test_year\", y=\"actual_precision\", color=\"type\")) +\n",
     " geom_line(stats, aes(x='SENTENCE_END_YEAR', y = 'RECID_PERCENT')) + \n",
     " xlab('Year') + ylab(\"% of Recidivation & Model Precision\")\n",
     ")"
@@ -5209,7 +5553,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "We should think of the % of recidivism as a baseline precision per year for our models. Ie, if our model predicted 100 random prisioners as likely to recidivate, the precision we'd expect would be the percentage who ended up recidivating for that year.\n",
     "\n",
@@ -5218,21 +5565,28 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "To continue examining the cause for the poor predictability of recidivism in the mid 1990s, let us look at the next flux of prisoners by year."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 216,
-   "metadata": {},
+   "execution_count": 83,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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uREBAALKzs3Hjxg1cu3YN165dw/bt21FRUYFu3brBx8cHAwcOxMCBA9G1a1dW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\nZLZbgwAAAABJRU5ErkJggg==\n",
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nstdCZWha29y2bRsWLFiAe/fuwdzc/K3PXbhwIXbv3o0bN26ofHKM2G2zKlSk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\nA1xlTeHaAWd9AAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11812d550>"
+       "<matplotlib.figure.Figure at 0x11c74a4e0>"
       ]
      },
      "metadata": {},
@@ -5241,10 +5595,10 @@
     {
      "data": {
       "text/plain": [
-       "<ggplot: (-9223372036560758447)>"
+       "<ggplot: (298268168)>"
       ]
      },
-     "execution_count": 216,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5257,30 +5611,39 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "For every year, this plot shows the number of prisoners inducted - number released. The change around 1995 in this graph is even more drastic. From 1994 to 1996, the prison went from a net influx of 3459 individuals to a net efflux of 4432."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Finally, we can examine the the number of prisoners whose sentences began and ended by year, as well as the prison population change over time."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 84,
    "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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kGwaBF54hum4NvhtuxTH+8JPJm609fBj+lOu8V6Ep/af8rDc++yTMqy8EOed7\nbk4+LT53MHw+lSuv9bLwPj+l43XGTYi9FE7LzCay8sv2r0drOayKrD7MHkKIZJHkXwjR56JlG3CM\n73+j/gCbyw2Oy40Q3bQR19ze1YDn5unsabZpabFI7WG9dXcCVoCH/I9R7BjLee4z43rsg13oOY9y\nYzMvBF7lWt+VR3Qso6aa6OqVRNevxdq9Cy1nNPrEyXi+fyVazuguW4squgPHpCk4Jk3BDgaJfLOa\n6Ndf0Xr/X1CHZ+CYfizOGceh+HwEnnwUc2cTKT+6C33U6G5jejP0DqO1UUxzTD6i15ZMtm2z7O0w\n774T4vKrvRwz88jnfRxobImDk09z8a+nAj1a7VodkYW9pxk7HEZxuRiljWSPvZdWy0+K6otrjEKI\nIyPJvxCiT1nBAGZ1JZ4zz0t2KIdo3m3R1GhResoQjHcqsE2zVxOSM7NVHE6oqzGZMCl+yX9bZ5+n\ncOLkOu+VR9TZJxZOxcn13qu4p/U+JkbGc0yM6wccyKiuJLR0MUZFOfrYElwnnoJjwhTU9PQeH0vx\neHDNnIVr5iysvXuIfL2S6KovCS97G9xutPSMto4+MdxRajC38UVkJT9JuW3ArmlgWTYv/yvIqi8j\n3PhDX49G5nvizHPdVJQZ/GtRgBtv9cX0/dIy2zpFmU2N6KNzyVIz0dBoMLdSqhb3SZxCiN6R5F8I\n0af6Y4vP/SrKo6QNURh5bAH73ohi1lShF43t8XE0TWF0rkZdjcGESfFLyF4KvsZWcxu/SP0JLiUx\ni4jl6qM5z30mzwVfokgviHlisVFbQ2jpWxjlG3FMnU7qz/8DLSs7bnGpQ4biPnk+7pPnY27fhlFd\niXPaMSju2EpeXgu+xUR9PGP1orjFlEjRqM2iJwJUbTH44Z0p5BX03Z9vXVe46novf/1zC//+NMLs\ned3/7CluD0raEKzG7TA6F13RyVYz2Wpuo9Qhyb8Q/Ykk/0KIPhXPFp8fhj/Fb/k52316XEbBK8oN\nikt1VI8XLb+Q6KayXiX/0Fb6E4+OP7vM3XwdXcPK6Bq2mdu4M+U20tVhR3zcnpjvOpn10TKeCjzL\nHb5bD/u9Nhrq25L+DetwTJpK6s9+hTZyVJ/Gp2WPRMseGfP2qyKr2WiU85vUu/swqr4TDFg8ttDP\nnj02d9yVwoisvp+vkJmlceGlHl55IciYYp2s7O7PqWVmYTZ+1/FnlJZDvSWTfoXob6TVpxCiz8Sz\nxWd5tIKXg6/zYfgTFvofJ2gf2QRb27bZXG5QXNo2Uu8oGYdRvrHXx9vf8ac3q6Q2W3t4L/QR97T8\njf9s+SP/jnzBJH08v079OUXX6r5yAAAgAElEQVR6Qa9j6i1VUbnWdyV1RgPvhz/udBtz21b8Tz5C\n671/BiDlp/8L33U393ni31PVRi1PBZ7jIvf5jNTidyciUSzL5h8P+QmFbH78s8Qk/vvNnOVk3AQH\nix73Y8TQ/lPNzMLqkPyPpEHafQrR78jIvxCiz8Srxec+q4UnAs9wqutE5jhPYKH/Mf5fy33c6ruR\nDG14r465fZtFS4tNcWnb26BeMo7Q0sVYAT+qt+cTFHPzNfytNs3NNunp3ddI77H2sjq6llWRNVSa\n1WSow5nhmMaV3kvJUUcmvS49XR3G5d5LeCrwHOP0YvK1PACMHdsIvPMm0TVfo5dOIOXOn6Pnxrf1\naLzstppZ6H+cmc5jOcU1L9nh9MqKzyJs22ryy9+mkTYkseN1iqLw/Ss9/OVPLbz9RojzLz58Bygt\nM5tw5aftX4/Scngj9DambQ747kpCHE0k+RdC9Jl4tPi0bIsnA88wXE3nfPfZaIrG3ak/5nH/09zT\n+jdu9l3XqzruivIomdkqQ4a2JVTa6DwUtxtj8yacU6b3+HjDM1Q8XoX6GoP09M47sOyzWtoT/i1m\nFenqUKY7pvJ9z4WM1kYlPeE/2LHO6ayPbuSJwCJ+ufcaws8vovXz5ejFpaTcfhd6Qf+tnw/ZIRa2\nPkaOls1lnov63fc2Fi37LBa/FuKs8zztP6eJ5ktpa//58P1t7T9Lxnc9p0XNzMJqasK2LBRVZZSW\ng4HJDquRHC32Mi0hRN+Ssh8hRJ+Jlm044lH/peH3qTXrucG7oH300KN4uNV3I8c5Z3Bf60L+Hf6i\nx8etKDMoKf1u/EPRNLSCIsyqyl7FqSgKuXldr/S722rmP/f9kWWhD8jTc7k75cf8LvU3XOg5j1y9\n69aXyXZxyyxCrc08v+rPmM27Sbv9LlJuuaPLxN+wDVotP7utZizbSnC0bSzb4gn/IgwMbvJeM2BH\nnd98NUj6cJXZ8+LbzrOniksdnHiqi+eeCuBv7fqaaplZYBpYu3cBkKqmkKakSumPEP2MjPwLIfqE\nHQx+2+Lz3F4fY7NRyeLQUm7yXstwrWOrSE3RuMRzASPVbJ4Nvsg2azsXus+LaSKwadpsqTA4YU7H\n8h69oIjourW9jjc3T6OmuvPkv8LYgkfx8F9pv+mTlp1WSwtG9Za2ydUxdsA5HKOhjvC772CuW8tl\ns/J55JQduE8dRTjyFUH/J4TsMGE7TNiOECbU/rXBd69/hJrBma75HOeckdAE/NXQm1SaNfwi5U68\nqjdh542nzZuirPoyyo/vTkHTkv/B8Ozz3GwuN3j+mQDX/6Dz9p/KkKHgdGI17kDLGAF8t9LvcfTN\n4nRCiJ6T5F8I0SeiFWXftvjsXWlIq+XnCf8iTnTOZqpzUpfbzXYdT4Y6nEcD/2SH2cj1vqvxKIdP\nfmurTaJRKCru+BaoFxQRWvIWdjSC4uj5aGtuvsbyj8NYlo2qdkyOqo0aCvX8PuvVH3zpOaLr1oCu\no5dOwDl1Oo7xk1A8PVup16ipJvTuOxgb16FPmETKHXczJb+Aq6JfsMWuQkVlqDIUl+rErbhwKS7c\nihsXLlzKd49paHwRWcmLwdd4O7yU013zOd55LA6lb//sfBr+nI/Cy/lxyi2M0DL69Fx9xTDa+vmf\nMNfZpy09e0J3tLX/vPfPLaxYHuGEuYe2/1QUpb3jj2NC2++sTPoVov/pH+8qQoijjlHW+xaflm3x\nVOA5UtUULvB0vzhYiWMsv0i5M+aJwBXlBnn5Gh5PxwRd+3biqllbiz6m5y0/c/N1QiHY2WSReVBX\nliqzhmMdPZ9LEIvo5k1E168l5Se/wPb7ia79muCrLxD41yL0knE4p05HnzgZ1dP1KLhRubkt6a8o\nxzFpKil3/arDqrlz3bM4f9g5NDc3Y5qxtTQ9z3MWp7pO4uPIcl4PLead0DJOc5/CbOfxOJX4L1BV\nHq3g+eDLXOX5PmP1MXE/fqJ89F6YQMDmnPOP/A5OPGVla5x/sYfXXgoyYYqDtLRDP8iqmdltvf6/\nNUrLYUXkKyzb6vNF6oQQsZHfRCFE3B1pi8/3wx+zxajkRu81MY8Uj9AyuDv1xwxTh/F/W/+HzcaW\nLretKI9SPO7Q4ypOJ1rOaIya3tX9DxmqkpamUFfTMTkO22EazG0U9kHbTtuyCL7+Es7jZ6Pn5uMY\nNwHvZQtI+88/4bvxVtS0NIJvvMK+3/2a1kf/TviLf2MF/G372jbRTWW0PHgvrX//HxRfCql3/wbf\ndTd3SPyPhFf1cJb7NP532m84yTWXd0LL+N2+P/Je6EPCdjgu5wDYYTbyaOCfzHedzAmu4+J23ETb\ntdNk2Tshzr/Ig8fb//5EnzDHydB0lX9/0vm1O7jXf6k+llbbT41Zl6gQhRDdkJF/IUTcmVt73+Kz\nyqjh9dBirvde1eOyDY/i4Ye+G3kl9Cb3tT7M5Z6Lme06vsM2oZBNTZXJWed1Xg6jFRZh9HLSL3zb\n77/W4JiZ35UN1Rh1KCjkavFJqA8U+eIzrN27cP/gjg6PK5qGo3Q8jtLx2BdfjlG5mejarwktfp3g\ni8+iF5dih4KYdbU4j5mJ9/tXoY3IjHt8+7kVN6e7T+Ek1xyWhz9nWfhDloU/4BTXiZzomtNtqdbh\ntFp+/u7/ByX6GM53nxXHqBPLtm1efSFIfqHOjOPif2ckHlRVYe5JLpa9HWL+GW50R8e7Z2pmFtbH\nH7R/naamMUYr5OvoGgr1/tkSVojBRpJ/IUTcRTeu71WLz4AV4HH/08xyzmSGc1qvzq0qKpd4vsdI\nNYt/BV9GRe0wEly52UDTIb+g83IkPb+I4MovsG27Vx14RufplG2Idnis2qxltJYT91IXOxgk9Pab\nuE87GzU1tcvtFE3DUVyKo7gU+6LLvv0gsBpUBe+CG9DSe7dWQm84FSenuE9krmsW/458wbLQB7wb\n/pCTnXOZ6Tymxx/4DNvgUf+TeBQ313qvHNClJevWRtlUZnD3r1P7bfcngGOPd/L2G0FWr4py7PEd\n58ZomdnYAT+WvxXVlwLADOdUloU+4CL3+f36dQkxWEjyL4SIu0jZ+h6P+tu2zdPB53Erbi7xXHDE\nMcx2HU+j1cS/I190SP4ryg2KxuiHjFjupxcUYgcCWE2Nba0Leyg3X+O9pSFM027v0lJt1FCgxX/U\nM/TuOygeD665J8W8j6KqOMaW4BhbEvd4esKhODjRNYfZzuP5IrKS98If8nZ4GSPUDCbq45jgGMdY\nfcxhPzDZts1zwZfYae3i56l34lIOnYQ6UITDbaP+J5/mIjO7f7cmdbkUjp/l4pMPwxwz09EhoVcz\nMkBRsBp3oBa2Jf/THJN5IfgqNWYtBTL6L0TSSfIvhIgrMxDAqNqC+4xzerTfR5HllEU38cvUn8Zt\nhHyKYxLvhT+ixWohVW0bGd9cHu1QknMwdegwlKHDMKore5f852kYUdi+zWTUaB3btqkya7jYObXX\nr6MzZlMj4U8/xHftTSj6wH0r1xWd2a7jmeWcSaO1kw1GGRuiZXzq/xwFKNbHMsExjon6uEPuCrwb\n/pBVkdXclXI7Q9UhyXkBcbL0rRCapnDamf1rkm9X5pzk5OMPwlRXmhSOOWC9DN2BOjwDs3EHemHb\npOv9pT+romsl+ReiHxi4fzGEEP1ScP03bRNne9Dis9ao59XgG1zlvYxsLX515wVaHqlKCt9ENzDb\ndTz79lls22p1Otn3QHpBEWZ1Jcyc1eNz+lJU0oer1NW0Jf+7rN202K0UxnnkP/jmK+hFY9EnTI7r\ncZNFURSytBFkaSM4xTWPiB2hwtjC+mgZH4Y/4cXgq4xQM5igj2OCo5SgHeKN0Nvc5L2WXD3+cykS\naWuDyScfhrnhVh8O58Aoi0kfrjFxioNPPgx3SP4B1BFZHTr+AEx3TuXd0Adc5D5PSn+ESDJJ/oUQ\ncRX4ZjWOkthbfAbtEI8FnuIY53RmOo+JayyqojLZMZG10XXMdh3P5nIDX4rCyJzDx6YXFBH+7ONe\nnzc3X6OuxuSEOW0tPlOVFIar6d3vGKPopjKMjetJvetXR20i5VScTHSMZ6JjPABN5k7Wf3tX4FH/\n50QxuMB97mHXgBgILMvm5X8FmDjZwfiJ/XOSb1fmnexi4X2t7Gm2GDrsu7kWWmYW5o6Dkn/HZF6U\n0h8h+oWBOzNKCNHv2LaN/5s1OMZPjHn7ZwMvoqFxmeeiPolpqmMyZUYFITtERblBcYl+yAJcB9MK\nirAad2D5W3t1ztw8jbratnaf1UYtBVp+3JJ02zTbWnueMAdtZE5cjjkQjNAyONk1lx+l3Myfh/ye\n/0j9Oae5Tk52WEfsy88jbGswueDSni3G1h8UjdXIGqmy/OOObT/VzCysph0dHjuw9EcIkVyS/Ash\n4sbc1oDZvBvHuNiS/5XR1XwTXceNvmv6bLJmiT4GHZ0NkXIqyqOMLe3+hqc2MgecTsya6l6dMzdf\nZ/tWk2jEpsqspkDP69VxOhNZ8Rn23j24zzw3bsccaJyKg5Fa9oC/6+FvtXjr1RBnnOPuMHI+UCiK\nwryTXXy+PEIkYrc/rmVlY+3ehR3t2PVqunMqqyNrsW374EMJIRJo4L3bCCH6rdDyj3EVjUUbOiym\n7b+MrOQE50xGaSP7LCZd0ZnoGM+X26rY02xTHEPyr2gael4BRnXv+v2PytWwbaipD1Nvbo1bvb8V\nCBB6501cp5/T3kZRDFxvvhpiyDCFuScP3C5F0491oqqw6otI+2PqiEywbaydjR23dUym2d4jC34J\nkWSS/Ash4sLcvYvwis9Iv+CSmLaP2FE2GZuZ9G1Nd1+a6phERXmU9AyF4RmxzUXQCop6nfy73QqZ\nWSrfVO3GxiZfz+3VcQ4WfvdtFF8KrjknxuV4Inmqthh8tSLCJZd721vCDkQOh8IJc5x88lG4fURf\n9aWg+FI6rPQLB5b+rElGqEKIb0nyL4SIi/C776CPzsM7JbbFuSqMzUBbK8e+NsFRirUljxHF/pj3\n0QuKMOtqsE2zV+fMzdeoqgkxShsZl5Ims3EH4U8/wnP+RTFPphb9k2navPhcgJmznBQUDfy+G7Pn\nuWjaYbF5k9H+mJqZhXVQ8g8w3TlFSn+ESDJJ/oUQR8zc2UTkqxV4zo59Bc/10Y2U6GPjvuptZ5y2\nC72ykOiYLTHvo+UXQDSK2VDfq3Pm5unsrHPEbXGv4BsvoxeXosc4mVr0T6Zp89ZrIVpbbM69YGD0\n9O/OkKEqU6c7+OSD7yb+aplZmAe1+wSY5pgipT9CJJkk/0KIIxZa9jZaQRGOknExbW/bNuuNje1t\nHPtafZ2JFXJSl/dvLNuKaR/V40XNGolZ07vSn9F5KpGdKYw2Cnq1/4Gi5RswyjfiOf/iAT/JdTBr\n3GHywF9b+erzCFde68XrO3r+BM89xcXG9QY7m9rulHU18j9ETaNIK+BrKf0RImmOnnceIURSmI3b\nia76Es+Z58acmO6wGtllNTNRT0zyX1FmkD0aWr1NPRpx1AuLMKp6l/x7clqxVQvX1iMb+bdNk+Br\nL+OcNRctu+8mRou+Y1k2H38Q4v/9nxZ8KQo//49Uxk0YWD39u5NfoJObr7H8o7bRfy0zG7OxEds6\n9MP2DOdUvpbSHyGSRpJ/IcQRCS19G31sCfqY4pj3WR/dSLaaxXAtfgtfHc7mTQbjxrko0gpYG10X\n8356fiFGdWWvkpR6pQYlq4mWuiPryhP596fYLftwn3HOER1HJMeunSYP/a2VpW+FuORyDzfe6iNt\nyNH5p3feyS6++DxCKGijZmZBNIK9d88h2+0v/amV0h8hkuLofAcSQiSEuW0r0TWretxzvq3kJ7YS\noSMVjdhUbTEoLtWZ4pjEmui6mJN5rXAM9r692M3NPT5vlVlDWq6f+treTRgGsAJ+Qkvfwn2mtPYc\naGzb5t+fhvnLH1vQNIW7f5PGcSe4juqyrSnTHbhcCl+uiKAOSwfdcUjHH/iu9Ee6/giRHElvMxCN\nRnnooYdYs2YNLS0tZGRkcNlll3HSSScBUFNTw3333Ud1dTVZWVnccsstTJ06tX3/5cuX8+STT7J7\n927GjRvHnXfeSWZmZvvzTz/9NO+88w6GYTBnzhx++MMf4nC03W5tbW3lgQceYNWqVXg8Hi666CIu\nuOCCxH4DhBjAQksXo5eMRy8oinmfoB1is1HFWa7T+zCy71RVGthA4Rid4dokXgm9wQ6rkWwtq9t9\n1eEZKL4UjJpKnOk9u0tRZdQyKj+PuneN7jfuQmjpYpSUNJyz5vX6GCLx9jRbPL8oQHWlwXkXeZg1\n13lUJ/37aZrC7Hkuln8UZs6JTtQRmW11/6WHlvdNd07l/dBHXOg+b1B8b4ToT5I+8m+aJunp6fzh\nD3/gueee4/bbb+fvf/87ZWVlGIbB73//e2bOnMmzzz7LFVdcwZ/+9Cf27Gm7jVhXV8f//M//cNtt\nt7Fo0SIKCgr47//+7/ZjL126lI8++oh77rmHhx9+mPr6ehYtWtT+/MKFC4lGozz++OP87ne/48UX\nX2TlypUJ/x4IMRAZDfVE163p8ah/WXQTThyM0Qv7KLKOKsoNCgp1nE6FDG04o9SRrImx9EdRlF71\n+4/aBvVmPePz02jebdPaEtsk4wOZTTuIfPYJnu9dLK09Bwjbtvny8wj3/HEf0ajNz36dyux5R/do\n/8FOmONkT7NF2Qbj244/h478A0z7dsGvg0t/LEvmAQjR15Ke/LvdbhYsWEB2dttS7RMmTGD8+PFs\n3LiRb775hnA4zKWXXorD4WDevHnk5eWxfPlyAD788ENmzJjB9OnTcblcXHXVVVRVVVFbWwvAu+++\nywUXXEB2djZpaWlcccUVvPfeewCEQiGWL1/ONddcg9frpaCggDPOOINly5Yl7XshxEASWvoW+oRJ\n6Hk9m9C6wShjnKMETUlMQltRZnRY1XeKY1LP6v4LijB7mPzXmw2YWEwZlYPDCXW9KP2JfLUCbVQu\njnETeryvSLx9+yyeeNjPS88FOP0sN7f9JIWMEYPvQ1tKqsr0Y518+mH4244/h7b7BBiqDvm268/a\n9sdqqw3u/e9Wqit7f7dMCNG9pCf/BwuFQmzevJn8/Hxqa2spKChAVb8Ls6ioiJqaGqCtJKiw8LvR\nQ6/XS3Z2dvvztbW1FBUVddh37969NDc309DQgG3b5Od/l7gUFha2f3AQQnTNqK3B2LAO9xk9G/W3\nbZv10Y0J6/IT8Fs01JsUj/su+Z/qmESNWUezdehExM7oBYWYWxuww+HuN/5WlVFDtppFiu5h1GiN\nupqeJf+2bRNd8zWOaTN6tJ9IjvKNUe75/1rYt8/mrl+lctJ8N6o6eEb7DzbvZBebygx2OkZ3OfIP\nbaU/X0fXEApZvPZSkPv+0srIHJURmf0uNRHiqJL0mv8DWZbFvffeS3FxMdOnT2fTpk34fL4O2/h8\nPhobG4G2DwqdPR8MBjt9fv+/g8EgoVAIr9fb5b4H2rlzJzt37mz/WlVVRowYcQSvtHOKoqANktv7\n+1/nYHm9R9u19S99C+fkabgOGvXv7rrWGnXss1uY7J6Ipvb996Nys4HLrZBf4ETT2pKxPDWXdHUY\n68yNnOyY2+0x1PxCUDXs+lr0TtYx6Oza1li1jHEUoGkaeQUO6mvNHl1/o64Wa2cT7unH9pufG/md\n7Vz5xgiPL/Rz0nw3Z5/vbf85G0jifW1z8zXGFOusqMrhtJZ9KJEwqsd7yHbHuKfx8ppv+PObe3Ao\nOrfckcq4Cc64xCCE6Fq/Sf5t2+bBBx9k9+7d/Nd//ReKouDxePD7/R228/v9eDweoK1kKBAIdHg+\nEAh0eP7A/fdv6/F4CAaDhyT6B+57oJdeeolHHnmk/evrr7+eO+644whebdeczsH1xpeWlpbsEBLm\naLm2wYpydpVvJPf3/41r2LBOt+nqun6w+xOKXAUUDI/PqrfdqalqZMIkLxkZHSfrHm8ex/rIRi4a\ndn5MxwkUFqJv38qw42d1+vzB17Z6by2Xpl/IsLRhjJ+g8+zKnQwdOjTm2u+dyxbjHltCRtGYmLZP\nJPmd/U7ZhiCPPbSbs88fxiWXD09QVH0nntf2nPMdLLx/B3MUD6ODAdw5ozo8v2+fydJFYZzLLyf9\nlEZ+c+1sXG4Z8RciEfpF8m/bNg899BBVVVX8/ve/b0/A8/LyeOmll7Asq730p6qqihNPPBGA/Px8\nKiu/q8UNBoNs3769vZQnLy+PqqoqJkxoq5mtrKxkyJAhDBs2rP0ctbW15OXltR97/78PdMkll7R3\nH4K2kf/mXrT+647P5zvkw87RStM00tLS2LdvH6bZ+1aIA8XRdG33Pf8MzmnHEPClEDjo96C76/rl\nvpWMd5T2ye9PZ9av8zNnnuuQ8423S1gSfJf6XQ341ENHJA8xOp+WjetRTjz1kKcOvrZ7zD3sMnaT\nFc6kubmZ4SNM9u01qarcxbD07kdWbdtm3+ef4T7x1IR9n2Ihv7MdVW2J8tB9+5h9optTTlf61bXq\nqb64tgVjbFJSFVZH5pGxuQJ3egbQ9vO98osIr77gZ+gwlXk/rWLViCX4AxMIBPv+rsmwLgYshBhM\n+kXyv3DhQsrLy/nDH/7QoRRn8uTJOJ1OXn75ZS644AJWrFhBTU0Nc+bMAeDkk0/m7rvvZvXq1UyY\nMIFnnnmGgoKC9gR+/vz5vPTSSxxzzDH4fD6ee+455s+fD7TdFZgzZw5PPfUUd911F01NTSxdupSf\n/OQnh8SXkZFBRkZG+9c7d+7skz9+tm0Pij+qBzJNc1C85qPl2hpbKohu3kTqL3572NfT2XVttfxU\nG7Vc4v5eQr4XrS0WjdtNCorUQ85XoOThVTysDa9jpvOYbo+l5hcS+nw5RjSKonYcnTz42m6OVOFR\nPGSQjmmaDEu38XgUqisjpA3p/u6PUVuN1bwbffLUfvkzI7+zUFtj8PD9rcw8wcm5F7iwOlnFdiCK\n97WdfaKTT948hnnbvsJhmuzeZfLSc0G2bDY44xw3J53qokUZy7J9z1IVqSFfz43buYUQXUt68t/Y\n2MjixYtxOBzceOON7Y9feumlXHbZZfz2t7/l/vvv57nnniMzM5Nf//rXDB06FIDc3FzuvPNOHnjg\nAZqbmyktLeWXv/xl+zHOOOMMmpqauPvuuzFNk9mzZ7NgwYL252+99Vbuv/9+rr/+ejweD5dccgnH\nHNN9IiDEYGTbNsElb+GYcRxaZvc98g+2wSjDp3jJ0xLzB766ysTlhuycQ0fbVUVlsmMia6LfxJT8\n6/mFEApi7diONjLn8Oc1ayjQ8lCVtg8JiqIwOk+jrtZkyvTu446uWYVWOAZ1yNDuNxYJ11Bn8Mj9\nfqYd4+SCSz2Dqo1nTx0/y8nS112s3+Qg/H6It98MkVeg8/PfpLZ3QhrK/q4/ayT5FyJBkp78Z2Zm\n8vrrr3f5fEFBAffcc0+Xz8+dO5e5czuftKcoCldffTVXX311p8+npKTwq1/9qmcBCzFIGRXlmNWV\neC9b0P3GnVgfLWO8XtqeFPe1qi0G+QV6lxMwpzgm8pj/aSJ2BKdy+BF5NS0NdXgGRnVl98m/UUuJ\nPrbDY7n5GrXV3Y+o2pZFZM3XuE85rdttReJtazBZeL+fiVMcXHy5JP7d8XhVphft5cWKE3DV+zn/\n+N0cOzWK6ndjmh4UjwfF5Wa6YyofRj7hAve58j0VIgGSnvwLIfo/27YJLXkT57HHo2X0vNOVaZts\nNMq4zHNxH0TXueotBuMmOrp8vlQvQUGhzNjEFMekbo+3f7Ev16yuOwQZtkGtWceZ7o7Je26+xmcf\nh7Es+7AtIM3aauy9e3DEcotAJFTjdpOF97dSOl7nsgWeQd3KsydOPTcF9cl1zPJ8ia9sF4HVIYhG\nOmwzJk3hpTtdbHz4t4xu9eG7+ka0rOwkRSzE0U+SfyFEt4yyDZj1dfgW3Nj9xp2oNmsJ2iHG66Vx\njqxzkYhNfZ3J2d9zd7mNU3EwwVHKmui6mJJ/Pb+Q8MfvH3abBnMbUQwKDiptys3TCYVgZ5NFZlbX\nk34ja1ahjylGTR08HXUGgp1NJg/9rZWisTpXXOOVxL8Hho/N4pLfZwEntj9mmyZ2OIQdDEI4REow\nRGH4ZTaemc6YraUoKSnJC1iIQUD6agkhDqtt1P8tnDNno6and79DJ9ZHyyjSCmLrrBMHdTUmtg25\n+Ycf35jqmMy66AZMu/uSHL2wCGtnE1bLvi63qTJryFYz8R70OocMVUhNU6g/zEq/tmW1Lew1VRb2\n6k9272pL/HPzNRZcPzD7+Pc3iqahen1owzPQckajjxnLjKGzWJveiHPWXFSfJP9C9CVJ/oUQh2Vs\n+AZz+1bc88/o9THWRzcywXHoAll9pWqLwahcDZfr8InaRH0cQTvEFqOq22OqWSPB7cao6XrbaqOG\nAv3QNQwURSE3T6OuxuhyX7OqEru1BceUad3GIhJjT7PFQ3/zkzVS45obfZL496FpjsnssnZTZzYk\nOxQhjnqS/AshumRbFsElb+E8YS7q0N71x95j7aXB2sokx/g4R9e1qkqDwjHdVzV6VS8l+ljWRtd1\nu62iquh5BZhVlV1uU2XWUKB1voBZbr5O3WFG/iNrVqGPLZFRz35i7562Ef/hGSrX3+xDd0ji35eG\nqUPbu/4IIfqWJP9CiC4Zmzdh7diB+9TTe32M9dGNDFWGkKOOjGNkXbMsm5pKg8Ix3S+oBW1df9ZE\n12Hbdrfb6gVjuhz532e1sMvaTWEnI//QNum3oc7ENA89j21ZRL+Rkp/+orXF4q9/biQ1TeH6W3w4\nnJL4J8J051S+jq6N6XdRCNF7kvwLIboUXfs1ekkpatqQXh9jvVHGRMf4hLXw277VIhSCgqLY+hlM\ncUyi2d5DfQzlBlpBIWZdLbYRPeS5arMGNy5Gqp2vgZBfqKMosG7NofsalZux/X4ck6fGFLPoO9GI\nzWML/bjcKjfdltJt6ePtNBYAACAASURBVJiIn2mOyey0dknpjxB9TJJ/IUSnbMsium4tjsm9r0GP\n2gZl0U1MTHC9/4hMldTU2N7ehqpDyNdyWRND6Y+eVwCWiVlfd+h5jVry9bwu1zHweBRmznLy4Xvh\nQ0Y2o6tXopeMR/X6YopZ9A3LsnnmnwFaW2xuvysDt1sS/0SS0h8hEkOSfyFEp8zqSuyAH8fEyb0+\nxhajEguTUr04jpEdXqz1/gea6pgcU/KvuN1oI0dhVB9a919lVFOg5R12/3mnuP5/9u47PKo6e/z4\ne2qmJCGBVGoIEHoPXRHpvQgICIqiC+oquq6iq/JdFBuuuqv+LLgoqCBIF5GmgA2RIqgQEggllACB\nQAIp0+/9/ZElMiaQnpmE83qeeSC3zZm5ycyZz5x7Ppw64SHl6B+1/6rHg2vfbxjbSm9/X1u3xs7h\ng27ue8BKcHDxysZE+Rpo6ktcJb5eCHEjkuRfCFEo575f83rOl+EC1AR3Eo31jQjQBJRjZNemqirH\njriJiS1Z4tbG0IozylnOe9KL3FYX0xDPn5J/j+rhhOfkNev9r6gVpqN1OwPfbXbkL3MfOYRqt6Nv\n1aZEMYvytf1HBz9sdTD5LxYioiTx95UWhmY0N8T5OgwhqjVJ/oUQBaiqimvfb2Uq+YG8i31b6iuv\n5CcjQ+VSplrikf8oXQSR2ojilf7ExOJOOeZVunPacxYnrmt2+rnaLb0DSNjn4vy5vNF/16970Ddt\nhtZcOXMgiIKSDrhYtdTG2DssNI679qzQQghRHUjyL4QowHPyOOqlTAxlGI0+70nnnHKelpXY4jPl\niJvAIA1h4SV/aWtjaFWslp+6mFjU7CyUC398S5DiOU64NoxAbdE1+w0a6mnQUMf3Wx1/lPy061ji\neEX5OJ3q4dOPcugzIID4LkZfhyOEEBVOkn8hRAGufb+ha9CwjF1+EgnXhhGhCy/HyK7v2JG8ev/S\ndBZqa2jFMc9xLitZ191OG1oTTXCwV+nPMfdxGhZj1P+KW/oEsPtnJ5d+O4TqcmJoUfrrKkTpXcpU\n+PC9bFq2NtB/sMnX4QhRYjExMTz00EMl2ufbb79Fo9Gwe/fu6243a9YsAgNl3pHqSJJ/IYSXvJKf\nX8s802yCK4mW+sob9YcryX/p6rXr6+pSUxvKwtzPsan2a26n0Wjy+v1fnfx7jhdZ73+1lq0NBNfQ\nsu2bSxiatURjksSzsjkcKh+9n0OtMC2332GptFa0QpSnVatW8fjjj/s6DFHFSPIvhPCinDmNkn4e\nY6vSJ/8O1UGy+0iltvjMzVU4e0ahYTH7+/+ZVqPlIetULigXeSPrbdI9F665rS6mYX7yn+XJ5ryS\nXqx6//z70mq4+RY9O0/WhVZS8lPZFEVl4fwcnE6Vu/8is/eKqsdmswHQvn17YmJifBuMqHIk+RdC\neHHu+xVd3Xpoa9Ys9TEOupLRoqGxvlE5RnZ9KUc9GI1Qu27pO7WE68J4POhhQrQhvJb9Fkfchc/m\nq28Qi5J2FsWWyxHnMYwYqK2LKtF9tQ89iqJq2Z/btNTxipJTVZUvlts4keLh3gesWKzyNigqxoIF\nC9Dr9aSlpXktv3jxIkajkblz57J9+3aGDx9O7dq1sVqttGvXjk8//dRr+ytlOl999RVjxowhODiY\nsWPHAgXLfopzvCvOnTvHbbfdhtVqJTo6mpdeeqnIx5SZmcmDDz5IdHQ0AQEBdOzYkU2bNpX0qRE+\nJq96Qggvrn2/lrnLz35nIk31cRg0pRuFL42UI24aNNSj05VtFNesMXO/dQrxhva8nf0+O5wF62J1\ndeqCTo/nRAqHnUeor6uHTlPCDx0HfqFT7eN8970HRVGL3l6Uix+/dbLjJyf3TLUSFi4tPUXFGTVq\nFHq9nmXLlnktX7FiBQBjx47l+PHj9OjRg3nz5vHll18yevRo7r33Xj7++OMCx5s6dSqNGjW6bqlP\naY63cuVKJk2axDPPPMP7779/zcfjdDrp168fa9eu5cUXX2TNmjW0aNGCIUOGsG/fvpI8NcLHKu+d\nWQjh9zzn01DOnilTvb+qqux3HWBAQN9yjKxox466aRxXPi9pOo2OMZaRROoiWJS7lLOecwwzDcyf\nvVej16OrVx/3saMcqX+WhvqYEh1fdTlxJfxOj2Ht2bZE4WCim+YtpcVkRdv/u4svV9m4424LMaUs\nDxOiuGrUqMHgwYNZvHix1+j84sWL6d+/PzVr1mT8+PH5y1VVpWfPnpw6dYq5c+cyefJkr+MNHz6c\nOXPmXPc+S3K83r17869//QuAAQMGkJaWxgsvvMDUqVPRaguODS9atIhff/2V3377jRYtWuTvl5yc\nzOzZs1m6dGkxnxnhazLyL4TI5/r9V7RR0ejCI0t9jFPOVDKUzEqt93e5VE4c95S4v39Rbg7ozl+t\nf+FH53bm5X6CQ/1jci59w1hcJ45y1HmMhvrrz+xbIN6kRFBVanZsRsdORq9Jv0TFOHnczaIFOQwc\naqJdB2npKSrHhAkT2L59OydOnADgzJkzfPfdd0yYMAGAjIwMpk+fToMGDTAYDBgMBj744AMOHTpU\n4FhDhgwp8v5KcrxRo0Z5/TxmzBhSU1M5depUocfetGkTrVu3Ji4uDrfbnX/r168fu3btKjI24T8k\n+RdC5CuPib325P5GHV00odqQcoqqaKdOeFAVqB9T/qO5TQ1NeDxwOmc8Z3kj6x0uKhlAXt3/6ewU\n7KqjRBf7Arh+24OhZWs0BiM9ewdw+JCb1JPuco9d5LmQ7uGjuTl0iDdya7/KmW1aCIChQ4ditVpZ\nsmQJAEuXLsVkMjFy5EgA7r77bhYvXszjjz/Opk2b2LVrF1OmTMFuL9hxLDKy6EGZkhwvIiKi0OOf\nOXOm0GOnp6ezd+/e/A8VV24vvPACJ0+eLDI24T/ke08hBADKxYt4Tp3AcvvEMh3n15zfaWVoUU5R\nFc+xo25q19FhMlVM15ZIXThPBE7nw9xP+VfWW0y13k2DmIacjPRQy24h0KiFYk7QqzqduA7sw3rH\n3QBEReto2kLPd1sc3DFZXpLL24V0D++9mU3d+jpuG2eWlp6iUpnNZkaOHMmSJUuYMWMGS5YsYdiw\nYVitVux2O2vXruWNN97g4Ycfzt9HUZRCj1XU725Jj3fu3Dmvn69cmBwdHV3o9jVr1qRNmzZ8+OGH\n141D+D8Z+RdCAODc/yvasHC00bVLfYwcJZeD9mRaGqtOf//ismgtPGi9j7aGVryZ/R57DMmc6dWM\neqc8XH5pFvbNG1EdRZfvuBL3g1aLvukfz1GvPgH8+ouLzIzC36RF6VxJ/KNq65h8r7XMF4MLURoT\nJkxg7969bNy4kZ9//jm/5MfhcKAoCkbjH2VoWVlZrFmzplT3U9LjrVq1yuvn5cuXU7t2berWrVvo\n9n379uXo0aPUrl2b+Pj4AjdRdcgwkxACyKv3N7RuV+KR0ctKFofdR0h2H+WQ+zBWrZVYfQxUUh6r\nKCopRz3Ed6n4Om6dRsc4821E6yL5JHcxequOsR1GYvZ4sG9ah+PHbzH1HYSxS3c0+sJfXl2/7cXQ\nsg0awx8X+DaO0xMVreWHbx0MG2Wu8MdxI7h4wcP7b2UTFa1j8n3Sy1/4Tr9+/ahVqxZTpkwhJCSE\nQYMGAXkXBHfq1IlXXnmF8PBw9Ho9r7zyCjVq1CgwKl8cJT3eli1beOKJJ+jXrx9ff/01n376Ke+8\n806hF/sC3HXXXcydO5devXrx+OOPExcXR2ZmJnv37sXpdPLyyy+XOGbhG5L8CyFQLl/Cc/wY5uG3\nFbltpnKJZPcRDruPcth9hDTlPBaNmca6WG42deOm8B7osnV48FRC5HAuTcGWq5Z6cq+S0mg03BJw\nE+HacD63raCVpRUBXawYO8Tj2PYD9o1rcXy/BdOAIRjadURz1Rup6nDgStyP9c57Cx6zj4lVS3Pp\nN9CEySyJallcvJA34h8RpWPyX6wYJPEXPmQwGBgzZgxz587l3nvv9RqZ/+yzz5g2bRqTJ0+mVq1a\nTJ8+nezsbF577bVS3VdJjjd37lw++OAD3n33XYKCgpg9ezYPPvjgNY8dEBDAli1bmDVrFi+++CJn\nzpwhLCyM9u3bX3c/4X80qqpKg+kSSk9Pr5DjBgUFkZWVVSHH9jc6nY7Q0FAyMjLweConSfQlfz+3\njp9+wL5lE8HPPF9g5D9DySTZfST/lq5cIFBjpbE+lsb6RjTRxxKtjUKr0frkvG7/0cG33zj4x6zg\nSrm/P/vzuVVtNuzfbcbx3Ra0YeGYBw1D37wlGo0G597d2FZ+TvA/Xy7wzYDbrfLyrMvc0juAnr1N\nlf0wilRV/mYvXlTyEv8ILXdPLX3i7+9/s+Wpqpzb8hAWFubrEITwORn5F0L8b2KvtgUS/2Pu4/w7\n+x2sGitN9I3oE3ALjfWxRGkj/ebCycqo9y8JjdmMeeBQAnr0xL55Izkf/xdd/RjMg4fndflp1bbQ\nkiC9XsNNtwTw/VYHPW4JkPr0Usj4X+IfHl62xF8IIaozSf6FuMEpOdm4jyQT2HdQgXU/OH6ihb4p\n06xT/CbZ/7NjRz30HeB/7Ru1QcFYRo4l4OZbsW9aR/a7/wHAet8D19ynaw8j32yw8/teF+3jpRd9\nSVyd+N8jib8QQlyTdPsR4gbnStiHxmJF1zDWa7lNtbHX9TvdA7r6beKfmaGQcUGptHr/0tDVCsM6\n4S6CHnsK04Ah6Bs3vea2ZouWzt0D+G6zA6nILL7MDIX338qmVtj/En+jf/6+CiGEP5DkX4gbnGvf\nrxhatfG6MBXgF+evmDUmWuorb6bekko56sZi1RAe6f8vZbroOpj6DkSju36J0s29jKSe8nD0cPWu\nvS4vmRl5I/41a2m5Z5ok/kIIURT/HS4TQlQ41W7Dfegg1nunFVi33bmTLsZ4dBr/qaf/syv1/v76\nzURp1Kylo017A99tttOoSaCvw/FrVxL/0Jp5ib9REn/hRyrignG9Xo/ZLO2ARdn4/3CZEKLCuBIT\n0BiN6BvFeS1P9ZzmuOckXY2dfRRZ8Rw76vHrkp/SuqVPAIkJbs6lyej/tVzKzCv1CampZcr9kvgL\nIURxSfIvxA3M9fuv6Fu2LlCKst2xk0a6hkTqwn0UWdFsNpUzqR4aNqp+yX/9BnpiYnV8v6XoGYNv\nRLk5eSP+NUK0TJERfyGEKBFJ/oW4QalOJ66kAxhbt/Na7lLd7HTtoVuAf4/6nzjmRqeHOvX8tyyp\nLHr1NbFrh5P08zL6fzVFUVn8aS7GAA1T7rcSECCJvxBClIQk/0LcoFwHD4BWgz7O+4Le3137UVQP\n7Q1tfBRZ8Rw76qZ+Ax16ffVM/lq00tMwVs+aFTZfh+JXvt/i4OhhN3dOsUjiL4QQpVCq5D82Npbf\nfvut0HX79+8nNja20HVCCP/h2vcbhuYt0RgMXsu3O3fS0dieAI3/9c6/2rEj1bPk5wqNRsPIsWaS\nDrg5sN/l63D8QspRN+vW2Bl7h4XwiOr5jY8QQlS0UiX/KSkpOByF16Lm5uZy8uTJMgUlhKhYqtuF\n68A+DH8q+bmoXOSgO5nufn6hr9utciLFXa2Tf4CoaB039Qrgi+U2XK4bu+9/TrbCpx/l0LWHkXYd\nZAI0Ia7Wq1cv3n//fVJSUtBoNPTs2dNr/VNPPcXdd9+d/3NMTAxms5mgoCBCQ0Pp3Lkzr732Gk6n\nM3+bWbNmMX78+AL3FRMTw4YNG/J/PnLkCHfccQcREREEBwfTvHlzZs2axfHjxwkMDMy/abVazGZz\n/s+LFi1iwYIF6HQ6r+0CAwPZtWsXAHfffTdGo5GgoCBCQkJo1qwZjzzyCGlpacV6XlwuFy+++CLN\nmjXDarVSr149br/9dvbt25e/zc6dO+nXrx9BQUEEBQXRr18/du7c6XUcjUZDUlKS17IFCxbQtWtX\nr+clJCSEixcv5i/bsGEDMTExALRs2TL/8RkMBoxGY/7P999/f7EeT3kpdvJvt9u5ePEiFy5cAODy\n5ctcvHjR63b69GlWr15N7dq1KyxgIUTZuZMPgduDoVlLr+XbHbuI1kZRX1fPR5EVT+opD243NGhY\nvZN/gP6DTDidKt9tvnEv/r1S5x8YpGXYbdLmUIiiJCQksH79+utus2rVKrKysjh//jxvvPEGixcv\nZsiQISWaYPDo0aN07tyZWrVqsWfPHi5fvsz69etJT08nMzOT7Ozs/FtERATr16/P/3nixIkAdOrU\nyWu77OxsOnXqlH8fjz32GFlZWWRmZvLFF1+Qnp5Ox44dOXv2bJHxjRs3jqVLlzJ//nwyMzNJSkpi\n6NChrF27FoAdO3bQu3dvBg4cyKlTpzh16hQDBw6kd+/e7Nixo9jPwxU6nY45c+YUui4hISH/8Y0e\nPZqnn346/+f333+/xPdVFsVO/ufMmUN4eDgRERFoNBoGDBhAeHi4161evXrMmTOH++67ryJjFkKU\nkWvfr+ibNkcT8Edpj6Iq/OzcRbeAzn7fN//YETfRtbWYzf4dZ3kwmTUMHWlm80Y7Fy8qvg7HJ77d\n7CDlSF6dv8FQ/c+5EGX11FNP8cwzzxQrkdfr9dx0002sWLGCbdu2sW7dumLfz6xZs+jcuTNvv/02\ndevWBfJGwP/f//t/tG3bttTxX0vTpk35+OOPqVmzJq+//vp1t92yZQvr1q1jzZo1dOvWDYPBgNVq\n5a677uIf//gHADNmzGDSpEn8/e9/p0aNGtSoUYO///3vTJw4kRkzZpQ4vhkzZvDOO+9w+vTpUj2+\nylLsYbORI0cSExODqqpMmTKFZ599lkaNGnltYzQaad68Oe3atbvGUYQQvqZ6PLj2/455xGiv5Qfd\nyWSpWXQydPBRZMWXcsRDTDUv+blah04Gft6m48uVNibfZ/V1OJXq2BE3G760M/EeC2HhUucvRHFM\nmzaNd999l6VLlzJu3Lhi7RMTE0PHjh35/vvvGTJkSLH22bRpEy+99FJZQi0xvV7PiBEj2LRp03W3\n27RpE507d6ZBgwaFrs/NzeXHH3/kn//8Z4F148aNo1+/fthsthJNqta2bVtGjBjB7Nmzee+994q9\nX2Ur9rtn27Zt8z/FaTQahgwZQlhYWIUFJoSoGO6jh1EddvQtWnkt/8m5gzaGVgRq/Tu5VFWVY0fd\njBxz45R/aDQaRo218O85WRxKdBHX3FD0TtVAdpbCwvk5dLvJSNv2Uucvqpa0h/9S7sc0tetA3UeL\nHpE2Go0899xzzJw5k9GjRxe5/RW1a9f2qlkvyoULF8pc6r17925CQkK8lqWmpmK1Xvu9qDhxFhVb\nRkYGiqIUuk10dDSKonDx4kXq1KlTxCPw9vzzz9OmTRsef/zxEu1XmUp1we/kyZMl8ReiinLt+xV9\n46ZozZb8ZdlKDr+7Eujm5xf6Apw/p5CTrVb7i33/rHZdHT16Glm13IbbXf0v/lUUlcWf5BIUrGXY\nqBvng54Q5WXSpEkYjUbmz59f7H1Onz5NzZo1ATAYDLhcBTuNuVwuDP/rElerVq0yl7jEx8eTmZnp\ndbte4v/nOK+lqNhCQ0PRarWFbnPmzBm0Wi2hoaFA3rcNf34urn4ertaoUSMmT57M//3f/103Pl8q\nVfJvs9l4+umniYuLw2KxoNPpCtyEEP5HVRRc+3/D0Nq7FnOX8xdqaIJpqm/io8iK79gRN6E1NYSE\n3njTlAwYYiI3R+WHrdX/4t+tXzs4npJX56+XOn8hSkyr1fLiiy/y/PPPY7fbi9z++PHj/PLLL/md\ngurXr8+xY8e8tsnNzSUtLS2/lKZ///4sX768/IO/Do/HwxdffFGgo9Gf9e/fn507d3LixIlC11ss\nFrp3787SpUsLrPv888/p3r07FkveIFlhz8XRo0evWVI0c+ZM1qxZw++//16ch1TpSjV09te//pXP\nPvuMCRMm0KJFC4xG+TpWiKrA/vV6cLu9kn9VVfnJuZOuAZ3Qavw/oa7u/f2vx2zRMmSEidXLbHTo\nZKRGiP+fr9I4ctjNxq/s3HmvhVphMpgkqqbIt/9b7sfU60v22jdixAheeeUVFi5cyNChQwvdxuPx\nsHPnTqZPn063bt0YPHgwAIMGDeKRRx5h3rx53HXXXTgcDp566ini4+Pzr/mcNWsWnTp14tFHH2XG\njBnUrl2bEydO8Prrr3PvvffSpk35ThaZnJzM7NmzSU9P57HHHrvutr1792bQoEGMHDmS9957jw4d\nOuByuVi1ahUnT57kqaee4tVXX6Vv3740adKEv/zlL6iqyocffsjChQv55ptv8o81ceJEXnjhBdq0\naUODBg3YtWsXH374IQsWLCj0vqOjo3nooYd49dVXCQwMLM+noFyU6h30yy+/5LXXXuOhhx4q73iE\nEBXElXwQxzcbsN4zFa31jxej456TnFXS6GrsdJ29/UfKUTc9e/v3BGQVKb6LkZ+3OflylY1J9/j3\n9RmlkZWlsGh+Dj16GmndVgaWhCirl19+mVtvvbXA8lGjRqHX69HpdDRu3JixY8fy6KOP5nd7CwsL\nY8OGDcyYMYMZM2ZgNBrp2bMny5cvz98mNjaWHTt2MHPmTNq2bYvD4aBOnTpMmDCBxo0bFyu+Xbt2\nFUiQFy5cyMiRIwF44403eOedd9BoNERHRzNgwAB++eUXoqOjizz20qVLmTNnDnfddRenTp0iNDSU\nm266iZkzZwLQrVs3Nm/ezLPPPsusWbMA6NKlC5s3b/bq4f/MM8/g8Xjo3bs36enp1K9fn1deeSX/\ng1JhZsyYUektPItLo5akoev/REVF8cknn9C/f/+KiMnvpaenV8hxg4KCyMrKqpBj+xudTkdoaCgZ\nGRl4PB5fh1PhfH1ulcuXyHrjFYzxXTAPHem1bnHuci4oF3kocGqZ76eiz+vlywrPP32Zvz8dRHRt\n/xgR9sW5PXXCzZv/ymbaw1Yax1XOxb+V8TerKCrz3s3BZlP5698C0et9V+7j67/ZynQjvR5X5vWK\nFfH7o9frS9R9RojClOo74wceeIBPP/20vGMRQlQAVVHIXbQAba0wTIOGea1zqA5+ce71+xl9r0g5\n4sZs0RAZVT3LXYqrbn09XbobWbXMhsdTfS7+3bLJwcnjnrw6fx8m/kIIUZ2VquzHYrHwww8/0L17\nd/r27VugRZNGo+Fvf/tbuQQoREXLUrI440kjzlC8ryirGvvX6/GcSSXob0+h+dPF+Hudv6PT6Ght\naHWNvf3LsSNuYhrq0GolMRw0zMSc57PY9r2DnreafB1OmR0+5GLTOjt33WelZi3/+FZHCOHfXnrp\npULnGWjQoAEJCQk+iKhqKFXy/+STTwJw4sQJfv755wLrJfkXVclWx4987/iRl2rMwqipXv3TXYeS\n/qjzDy3YFm27cyedDB0xaKrGBbRJiW663SR14ADWQC2Dh5tYu9pGu45GgoOr7rch2VkKny3I5aZb\nAmjVpnr9DQohKs7TTz/N008/7eswqpxSvVsoinLdW3WvGRTVy0H3Iew4SHAd8HUo5Uq5fInczz4m\n4JY+GFq0LrA+zXOOI55jdAuoGiU/6ec9nE9TaN5SksMrOnc3Ehau46vVNl+HUmqqqrJ0US7BNbQM\nHlH1v8EQQgh/V3WHioQoB9lKDic8p6itjWaXa6+vwyk316vzv2K7cycNdPWooyu6Y4I/SExwExau\nJTxCSkKu0Go1jBpnZs8uF8eOuH0dTqls/9FJ8iE3d9wtdf5CCFEZSvVd//fff1/kNkVNviCEPzjk\nPoxJY2KkeQgf5MwnV8nForUUvaOfu16dP4BH9bDD+QtDTAN8EF3pJO530byVjPr/WYMYPZ265l38\n++iMwCp1PUTaWQ9frrQxcqyZiEj5UCeql8Jmfy0rrVbGbEXZlSr579WrFxqNhqu7hF7p+XqFlP6I\nqiDJfYg4fWOa6eMwayz86tpH94Auvg6rTP6o859WaJ0/QII7EYdqp6OxXSVHVzoOh8qRw2569blx\n+/tfz+DheRf/bv/RSY+eVeM5crtUFs3PoWkLA527yXUcovoxmaSMTfinUiX/e/cWLI/IyMhg48aN\nrFixgrlz55Y5MCEqmqqqJLkO0c90K1qNlo6Gdux27anSyb93nf+1O/j85NhJe0NbzJqq8eaUfNCN\nTguxjavGhcmVLTBIy4ChJjastdO+owGL1f9HB9d/aScnR2Xaw+YCg0dCCCEqTqneSdu2bVvo8l69\nemGxWJg7d26hs8kJ4U/OK+lcVDNopo8DIN7YntezfyRTuUSItoaPoyu5/Dr/sPBr1vkDZCqXSHAn\n8kjgA5UXXBklJbiIa2ZAb5Ak8Vq63WRk+w8ONq23M3KMf5euHUp08f1WB1MfsmIN9P8PKkKUhs1W\n/hfi63Q6jEb5pkyUTbm/6nbv3p1169aV92GFKHdJ7kOEaWsRrsub8bGBrh5h2lr84qyaF/7aN63D\ncyYV66R7Cq3zv2KHczfh2jAa6RpWYnSlp6oqiQkumreSUf/r0ek0DL/NzE/fOzl31n/LLnOyFZZ8\nmsstfQJo0lSu4RDVl9vtLveblFSL8lDuyf/q1aupWbPwOmMh/EmSO5mm+ib5P2s0GuIN7dldBbv+\nuA4l4ti8Ecv4u9CGhF5zO4/q4WfnLroZO1eZUoszqQqXMlWatZBEsShNWxiIa6bny1X+2fpTVVWW\nfpZLUA0tA4dWjZIzIYSobko1lDZ8+PACy5xOJwcPHuTEiRO8+uqrZQ5MiIrkUT0cch1mouV2r+Xx\nxvasd3zNWc85onQRPoquZJRLmeQuKrrO36k6+SjnU+yqna7G+EqMsGwSE1zUrqujRoiUhxTHsNvM\nvP5SFgcPuGjqZx+Yft7m5FCim789GSRtPYUQwkdKlfxfvny5wKihyWSib9++jBkzhgEDqk77QHFj\nOu45iQMHTfWNvZZH6iKop6vLbucehpoH+ig6b6qioObkoOZko2Rn5f9fzc5Gyc3GfSQZbXjEdev8\ns5Uc5uZ8RJaajMhmhgAAIABJREFUzWOBDxGkDarER1A2iQkumreUkp/iiozS0e0mI2tW2nisqR6d\nzj+S7LSzHtassDFitJmIKGnrKYQQvlKqd9Rvv/22nMMQonIluQ/RQFev0J7+8Yb2/OD8iSGmAZVe\nGuNJO4vjh6140s6iZmej5mSh2mxwpa2uRoPGGvi/mxVtYBD6Rk0w3drvmnX+F5UM3sn+L0aNkccC\nHyK4CiX+OdkKx495GDrS7OtQqpT+g03s2ZXFjp+cdL/Z960/3S6VRQtyadrcQJcecrGiEEL4UpmH\n02w2G5mZmYSEhGA2yxu0qBqSXIfyu/z8WUdjO1bb13Lcc5IYff1Kicd9IgXHlk24Evahj22CoVmL\nvAQ/MBDtlWQ/MBCNyYymBJO8pHrO8G72f4nWRXGf9S5MVaS15xUHE92YLRrqx8hIcUlYA7X0H/xH\n60+zxbclU+vX2sm+rDDtIWuVudZECCGqq1In/2vXruW5555j7969qKqKRqOhffv2PPfccwwePLg8\nY/Q7RqORgIDyH03T6/UEBVWdUdmyuJIAWK1Wr8niKkOuYiMl8wTja40hKKDg8x1EEM0dcfzGfloH\ntSyX+yzs3KqqiiPpAJc3rMVxMBFz2/aEPjmTgIaNyuU+k+yHePPSu7Q1t+a+mpPRayq+dKa8z+vh\nQxdo1cZMjRrBZT5WRfHXv9sBgwP5edtZvtuiMHZC2VvXlvbcJibY+X5LJo88EU5UdNX58Omv57Ui\n+PL1WJSPXr168fPPP6PX6wkICKBTp0689dZbxMXF8e2339K7d28sFu9vuj/++GNGjx7NrFmzSEpK\nYsmSJQWOe/fdd/PZZ595tRc1mUykp6d7bdeuXTtOnz5Namqq18zGqampPPbYY2zduhW73U50dDRT\npkzhySefBCAmJoa0tDR0V31z3a9fP1atWkVKSgoNGzbEarWi1WoxmUy0a9eOadOmMXr06GI9L3v2\n7GHWrFn8+OOPKIpCTEwMkydPZvr06eh0OpxOJ7Nnz2bhwoWcPXuWqKgoJk2axMyZM/Mf87Wen5iY\nGN5//30GDhzIggULuOeee3j22WeZPXt2/jZdu3bl/vvvx2AwMG3aNCDvvT83Nxer1Zq/3YEDB6hf\nv3IGG6GUyf/q1asZPXo0Xbt25Y033iAyMpKzZ8+ybNkyhg8fzooVKxgxYkR5x+o3nE4nTqez3I8b\nFBREVlZWuR/XH13pVZyTk1Pprct+d+3HgIFIRzhZzsKf73baNnyZs4GhugHoNGUfdb763KqKgith\nH44tm/CknsTQPp6gvz+NLioaJ+Ash9+BX537WJC7iJ4BPRhpGIItu3K6v5TneVUUlf2/2Rh1u9mv\n/y78+e92yIgAPp6XTcfOGsIjyvZ7XJpzm5Ot8NH7WfTsHUDd+i6yslxliqEy+fN5LW++fD2ubBUx\ncOcv/vOf/3D//feTm5vLtGnTmDJlCj/++CMAERERnD17tlTHfeyxx3jllVeuuX7v3r3s37+fwMBA\nvvrqK0aOHJm/7s4776R58+YcOXIEi8XCwYMHSUhI8Np/1apVDBx47Wvs0tPTMZlM5OTk8NVXX3H/\n/feTmJjIs88+e924d+zYQZ8+fXjqqaf473//S2RkJAcOHOD5558nKyuLkJAQxo8fT0pKCqtXr6ZV\nq1YkJCRwzz33kJCQwMqVK4v5DOUJDQ3lzTffZPr06YSHh3utmzhxIhMnTgQgKSmJ5s2bk52dXaLj\nl6dSJf/PPfccEyZMYOHChV7LH3nkESZNmsSsWbOqdfIvqrYk1yGa6GOvm9S3M7RhqW0lh9yHaW5o\nWi73q3o8uPbswr71G5SLFzB26Yb1znvRlnNr3B8cP7HMtpoRpsH0MfUq12NXpuPHPNhsKk2by8W+\npdW8lZ7YxnrWrrZxz9TASr3vK209A4O1DJK2nkJUGovFwvjx47n99tuL3rgczJ8/n379+tGgQQPm\nz5/vlfzv3LmTOXPm5H+L1qJFC1q0aFGq+7Fardx+++2oqsrkyZN58MEHr9ta/oknnuDOO+/0+pDQ\nokWL/BH8rVu38tVXX3Ho0CEaNGgAQJs2bVi5ciVxcXFs3bq1RBPWxsXFUadOHV588UX+85//lOox\nVpZSvasmJSUxZ86cQtfdeeedXideCH+T6D7ELQE9rruNRWumpaE5u517y5z8q04nWVu+5vLGr1Dt\ndgJ63EzATbeiLeeSAlVV+cq+ka8dW7nTMp5Oxg7levzKlpjgIiZWh8UqLT5LS6PJm/jrjVeySD7o\nqtRJtbb/eFVbT5mZWdyA/pL2cLkfs4OpHTPqPnrdbbKysli0aBGNGze+7nblwel08tlnn/Hmm2/S\noEEDbr31VtLS0oiMjATyJn596KGHePjhh+nevTuxsbFlvs9Ro0Zxxx13sGPHDgYNGlToNrm5uWzb\nto1Zs2Zd8zibNm2iS5cu+Yn/FQ0aNKBLly5s2rSpRMk/wAsvvEB8fDyPPfZYpZbxlFSp3lVr1qzJ\nwYMHC1138OBBmeRL+K2LykXOK+k00xed0Mcb2vObax9OtfSlCqqikP3fd7i8/ksCuvekxrPPYx40\nvNDEX1VVfnPu419ZbzIn698szP2crY4fOOQ6TI6Se9378ageFtuWs9XxA/db763yiT9AUoKL5i39\nq099VRRdR0fXHnmtPxWl4uu5L2UqLJqfw6qleSVb0tZTiMrx2GOPUaNGDYKDg9m5c6dXdca5c+cI\nCQnxuiUmJhbruP/5z3+89rs6IV6zZg12u52RI0fSo0cP6tWr53W/S5cupV+/frz66qvExcURFxfH\nhg0bvI4/ZswYr+PPnDnzuvEYjUbCwsK4ePHiNbfJyMhAURRq1659zW3S09OvuT46OrrAdQ3F0bx5\nc8aNG3fdDx3+oFTJ/7hx43j66aeZN28emZmZAFy6dIl58+bx7LPPMn78+HINUojykuRKJkRTg0ht\neJHbtjS0ADTsdx0o9f05d27Hc/oUkU/PwtS7HxpTwY5YqqqS4Erk1ew3WZC7iFh9Qzoa2qOoCj87\ndvJOzn958vL/8eyl2byXPY81tnX84vyVs540FFXBqTr5b87H7HMl8EjgAzQ3FN7FqCrJzFA4narQ\nTJL/cjFgiImMCwo7t5f/tUpXeDwq32+x8+oLl0lPV3jkiUA6d6u+9dVC+Js33niDS5cukZycjFar\nJTk5OX9dREQEmZmZXrfmzZsX67iPPvqo135bt27NXzd//nxuu+02rNa8Tl4TJ05k/vz5+etDQkJ4\n4YUX+P3337lw4QKjRo1izJgxXon78uXLvY5/9QWzhXE6naSnp193oDk0NBStVsvp06evuU1YWNg1\n1585c4awsDAADAYDLlfBQUCXy+V1cfMVs2bN4vPPPycpKem6j8OXSlX28/LLL3P8+HGmTp3KtGnT\n8p8YVVW57bbbeOmll8o7TiHKRZL7EM0MccVqN2jUGGhrbMVu1146GNuW+L6UrCzsX32BacBQ9KE1\noZCLBw+5DrPWvoHjnpN0N3ZhmvUeQrTenVncqps05RypnjOc8pzmhOcU2507yVKzMaDHrLFg1Bh4\nLPAhwnVhJY7THyUluKgRoiG6tpT8lIfAIC19B+a1/mzXwYjJXL5lOMeOuFn5eS6XMlWGjTTTubsR\nrVZKfcSN7b+Rb5f7MfX6otO2xo0b8+abb3LvvfdesyymPJw5c4aNGzdisViIiooCwOFwkJmZya5d\nu+jUqZPX9jVq1GDmzJm8+uqrHDt2rNRVIqtXr8ZgMNClS5drbmOxWOjRowfLly+nd+/ehW7Tt29f\n3njjDY4fP+5V+nP8+HF27NjBP//5TwDq16/PihUrvPbNzc0lLS2tQMnQle2nTp1a5AXJvlSq5D8g\nIIAVK1awb98+fvjhBzIyMqhZsyY33XQTrVu3Lu8YhSgXiqqQ5E7mdvOoYu8Tb+jA3JwPyVVyC50Q\n7HpsX65EW7MmAT16Flh31J3CV/aNHHIfposxnsmWO6ilK/yFUK/RU0dXmzq62nSmY/7yy8plUj1n\nOK+k097QpkrN2luUxP+V/EhP+PJz0y0BbP/RyeZNdoaMKJ85WbIuK6xdbWPPLheduxkZPNyENVA+\nsAnha4MGDSIyMpK5c+fSrl27IrdXFAW73e61zGS6/oX6n3zyCXXr1mX79u1er9Xjxo1j/vz5dOrU\niRkzZjBp0iRatGiBy+Xi3//+N6GhoTRr1qzEjyk3N5d169bx8MMP849//KPIDw+vvvoq/fr1o169\netx3332Eh4dz8OBBnnvuOd5991369OnDgAEDuO222/joo4+8uv0MGDCAPn36AHnP5SOPPMK8efO4\n6667cDgcPPXUU8THx9OoUeGtuZ955hkaNWpUrA9rvlDsV+nk5GQ6duzIunXr8pe1bt2aBx98kGee\neYYHHniAU6dO0bFjR44ePVohwQpRFic9qeSquTTVNyn2Pk31jTFrLPzq2lei+3IlH8S1dzfm0RO8\nZt496U7lvewP+Xf2OwRqAnk2aAaTLOOumfhfT7A2mOaGpvQM6FGtEn+XS+XQQTfNW0nJT3nSGzQM\nHWXi+60OLqSXvQ3rtu8czJl9mbOnFR56LJCxd1gk8RfCjzz55JPMmTMHm83GuXPnCAwM9Lq99dZb\n+dsuW7YMs9nsdXO73UBeOdGf971w4QILFizgwQcfJDo6mqioqPzb9OnTWbx4MXa7HZvNxpgxYwgN\nDaVevXp8++23rFu3zqvH/ahRo7yO3blzZ6/HERYWRnBwMA0aNOD999/n7bffzh+Vv56uXbvy7bff\n8tNPP9GkSRNCQkIYN24cXbp0ye8+tGzZMgYOHMjw4cOxWq0MGzaMgQMHsmzZMq/737BhAwsXLiQq\nKoomTZpw/vx5li9ffs0BqrCwMP72t79d97oEX9KoxZzR40pf1e++++6629166600b96cd999t1wC\n9EeluQikOG60vtKhoaFkZGRUWl/pjfbN7HX9zlNBfyvRfstzv+C0cprpgQ8Ua3vV5SLr9ZfQN22O\nZVReq7VLpiyWXVjJb679tDa0ZIhpAHV00SV+DP6uPM7rwUQX8+fm8NycGgQE+P/If1X6u1VVlfff\nysFi1TD5PmvRO1zlyrnd+8s5li/O4cIFD4OGmul2c/Us8alK57WsfPF67CtX6rgrQ0X8/uj1eszm\n8vnmTty4ij1Ms2nTJqZMmVLkdlOmTGHjxo1lCkqIipDkPkSzEoz6XxFvbE+y+yiZyqVibe/Y+jWq\n04F54DDcqpvPc1fy7NnncapOHg+czlTr3dUy8S8viQkuGjXRV4nEv6rRaDQMH21i/28ujhx2F3s/\nVVXJuOjhow/O8dZrl4muo+XJmcH0uCWgWib+QghRnRW7GCk1NfWatU1Xa9iwIampqWUKSojy5lAd\nHHWnMDCgb4n3baCrR5i2Fr849xY5aZbnfBr2zZuwTLiLnACFedkfcE5J58nwv1HXde2WYyKPqqok\n7ndzcy/pElNR6tTV06mrkTXLbTwyI9Arec/JVjh/TiH9vML5c57//auQfs6D0wl16xv569+CiYmV\n8h4hhO8FBhY+eeHcuXPzZ9QVBRU7+Q8MDOT8+fNFbpeenu5VyyWEPzjsPooWDbH6mBLvq9FoiDe0\nZ7fr+sm/qqrYVnyOvnEcF1rVYW72W5g1JmYEPUI9U12yXDdGCUFZnD+ncCFdoXkr/7xIqroYOMzE\nnOcu8/nCXFSV/ITflqui0UBIqIawcB1hEVriuxgICzcRFW2gcZNaXLqUWe1LQ4QQVUN2dravQ6iS\niv0OGx8fz+eff86oUdfvlLJkyRLi4+PLHJgQ5SnJnUwjfSwGTekuIo03tme942vOes4RpYsodBvX\nnl24U46R+sQdLMh+mzhDE+6yjCdAI6PYxZW430VEpJZaYTIxVEUKDtYy7DYze3Y5CY/Q0aadgbAI\nLeEROmqFaTEUMiOvTqeTEh8hhKgGip38//Wvf2XkyJE0b96cZ599Fp3O+81ZURR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T1NCk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\nvl5ZWVm67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Q5F+IBHLjMUKzZxHfupnA7fdidOtRq/H0rt0o/WoF1tbNGPm9EhSlEE2Pa9tEF39C5KN5\n6Nm5pD76z2hp6TUa65hznHcjH/BlbCUFRl/+NfWXTbqDTyLpuXnYRYUwdgIA+XoP5kU+IuiECMiJ\nkxANgiT/QiSIU1ZG8IVncUvLSL3/EbT2HWo9ppqSitF/ANHlSyT5F+IMrF07CL0+G7f0OP6rf44x\ncEiNavGjbpSFkUV8HF1EhtaBh1LukXKVatJz8gi/PRfXdVEUhSytC17FyxZrGwPNmt9oLYRIHEn+\nhUgA++ABgs89jRJIIeWBR1BTWyRsbM+I0ZQ/9Weco0dQWzePVoJCVIUbi3F0zkuUL1qIOWgo3rvu\nRw2kVHscx3VYEfuadyPz0VD5uf8aBhkFqIqU2lWXnpOHGyzHKTmIlt4eVVHJ17uxydoiyb8QDYQk\n/zVgmiaeHyxokgi6rpOamprwcZujE7N+gUDgu5ts605kyyaOP/MXvPm9aHPrHahmYo8Nt29/oh07\nweqvSb3sqoSOXVVybCZOfR6bTZnruhz5+7NEt28l/aFf4a3hlbGt0e3MOjqHg9YhLmlxAeenTMRU\nzQRH2zgk5NhMTSXYug36vr2k5HYFoEDpz9ul75Fy4v4nIURSSfJfA7FYjFgslvBxU1NTKSsrS/i4\nzZGmaZimSTAYxLbtOttP7KsvCL0xG8+Y8ZgXXUowGoNo4o8Nfdgoyj76AKUWNy/WhhybiVNfx2ZT\nF/1iKeFVX9P+n/4/oq1aE6/m8Wm7Nu9HFvBR9BNGmEO5K3UqLZRUosEoUaJ1FHXDlqhjU83OJbjp\nG9z+AwHIcbpwxD7K9uOFdNDaJyrcGqmLiTshGhu5pilEDbiuS/jD9wm9/gq+y6/Bd8nlddqNxxw4\nGDcaIb5hbZ3tQ4jGwtq7h/Bbb+C7/GrMLpnVfv0hu4Q/lj/B8tgK7gpM5Qb/NbRQ5cpWoug5eVjF\nhZX/bqO2ob2aziZraxKjEkKcIMm/ENXkWnFCr7xI9LNPCUy7C8+I0XW+T8Xrwxw4hNjnS+t8X0I0\nZG4kTGjWDIx+AzCHjazea12XL2Mr+d+yPxFQ/Pwm9RF6Gz3rKNLmS8/JxTlcglN6vPKxnnoPNsW3\nJDEqIcQJkvwLUQ1OsJzyZ5/AKtpO6r0PY/Sovw48npFjsAq3Yh88UG/7FKIhcV2X0OuzQVXxX3V9\nterHw26YF0Kv8EroNS7xns9dgWky219H1PYZKD4fVnFR5WP5Rne2WYXEXSuJkQkhQJJ/IarMLjlE\n+V//D2IxUh94FK1jp3rdv9axM1pWDtHlMvsvmqfY50uIb1xP4KZpKNWo3S6ydvC/ZX9kr72XR1Mf\nZLx3rHTyqUOKqqJl555S+tNNz8PBocgqTmJkQgiQ5F+IKrGKCyn/yx9Q27Un5Z6HUFu2SkocnpFj\niH+9ArcObjgXoiGz9uwi/M4/8F15HVpGxyq9xnZtPogs4M/lT9FTz+dXqQ/RWavaa0Xt6DldsU9K\n/j2Khzw9m02WlP4IkWyS/AtxFrHVX1P+zF8xBg4hcMsd1ZpxTDSj3wBQVGJrViYtBiHqmxMOEZo5\nA3PAIDxDhlfpNUecI/yl/Bk+jS5lmn8K1/uvwlSaZwvPZNBzcrH37cGNhCsf66n3YLPc9CtE0kny\nL8QZuK5LZOF8QrNn4rvkcvyXX12nHX2qQjEMzCHDiX2+JKlxCFFfXNcl/OrLYJj4rryuSq9ZGVvD\n/5T+EU3R+OfUR+hv9qnjKMUPaV0yQdOwdu6ofCxf78Eeex+ljrQNFiKZJPkX4jRcxyH82stEPl5A\n4Obb8YwZl+yQKpkjRmPv2YW1e2eyQxGizsWWLiK+dROBKdNQzJ+euY+4EWaF5vBi6BUmeSdyX+AO\nWqkt6ylScTJFN9C6ZJ1S999JyyBFCcjsvxBJJot8CXEa8Y3ria1ZScq9v0Dv3CXZ4ZxCS0tH755P\nbPlS9C5ZyQ5HiDpj7dpB+L238F87Ga19h5/cdqe1ixdCrwDwSMr9ZOkN6+e2OdJz8k6p+1cVtbLl\n51BzUBIjE6J5k5l/IU4jtmQx5qChDS7xP8EcOYbY6q9xQqFkhyJEnXBCQYKz/o45eBjmoKFn3s51\nWBD5hP8rf4Kuei7/lPqwJP4NhJ6Th7VrB671fXvPfKM7m61tOK6TxMiEaN4k+RfiB+z9e7EKt+IZ\ndU6yQzkjo2cfFH+A+MoVyQ5FiIRzXZfQqy+h+Hz4Lr/6jNuVOeU8dujPfBT5hFv8NzDZfy0eJXk3\n5ItTadk5YFnYe3dXPpavd6fMLWOfI+uVCJEskvwL8QPRpYvRu3avcjvBZFA0Dc+wUUSXL8V13WSH\nI0RCRRd/grV9K4GbpqIYp6/zt1yL54IvEnLC/KbFLxhoFtRzlOJsVJ8fNaPjKXX/LdUWdFIzZLVf\nIZJIkn8hTuIEy4mt+grP6HHJDuWszGEjcEoOYRVuS3YoQiSMtaOIyLy38V9zA1p6+9Nu47our4Xf\npMQ5zC/S76ON2qaeoxRVpWfnnZL8A/Q0pOWnEMkkyb8QJ4l9uRw1pQV6r4bfGlBt2Qqjdz9p+yma\nDCdYXlHnP2wUZsGZbwj9LPY5X8ZWckfgFlpp0s2nIdNzcrGLi3Cd72v88/XuFFpFRN1oEiMTovmS\n5F+I77i2TXTZZ5ijxia9n39VmSPHEN+wFqf0eLJDEaJWXMchNHsmakoKvkuvPON2W+LbmBt+m8n+\na8nSM+sxQlETek4ebiiIc+hg5WN5eg4KKtutoiRGJkTz1TgyHCHqQXzjetxgEHPoiGSHUmV61+6o\nbdOIfbk82aEIUSvRRQuxdhThv2kaimGcdpsS+zAzQrOY4BnLEHNgPUcoakJt1Rq1dVus4u2VjxmK\nQTc9l01xKf0RIhkk+RfiO9ElizAHD0X1+5MdSpUpioI5YjTRL5adclldiMbEKtpOZP57Ff3809JP\nu03EjfBs8HmytUwu9V5UzxGK2tBy87CKT53lz9d7sNmSm36FSAZJ/oUA7H17sIu2N+j2nmdiDh6G\nW16OtembZIfSoEXcCF/FVrHdKqTcCSY7HPEdp6yM4EvPY44ci9lvwOm3cR1mBmfjYHNrYDKqIh9d\njYmek4td9MObfrtzwDnIUedYkqISovmSFX6F4Lv2nt16oHXISHYo1ab6A5gFA4kuX4LRu2+yw2mQ\nvrUPMT34PMedUqLEcHBIVVLI0NrTQW1PhtaBDlp7MtQOpKiBZIfbbFTU+b+I2rIVvksuP+N28yIL\n2GYV8mjqg/gUXz1GKBJBz84jfHQOzrGjqK1aA9BBbU8rpSWb4lsY6RmW5AiFaF4k+RfNXkV7z68J\n3DQ12aHUmDlyDOV//T/swyVobdOSHU6Dsj6+kReDr9BNz+OR1AfQ0TnoHOKA/S377QMccL5lS3Q7\nh5wSHBxSlEDFyYDa/pSTg1Q1JdlvpcmJfvwh9u6dpDz8Tyj66T+OVsXWsiD6CXcFptFeO31JkGjY\n1PYdUPwBrOJCzAGDgYqSxYrVfrdK8i9EPZPkXzR7sRWfo7Zsid6zd7JDqTGtSxZax87EvliG7+LL\nkh1Og+C4Dh9GP2ZeZAEXec/jfM+5leUinbQMOmmnXuWxXOukk4JvOeB8y2fR7Rw86aSgg9qeAWZ/\nxpojURQlGW+rSbCPHCby3lvE168hcPPtaG3anna7PdZeXgrN4TLvxfQyetRzlCJRFEVBy8nFKi6q\nTP4Beuo9eDU8F8d1pJRLiHokyb9o1k609/ScM6HRtPc8HUVRMEeOITLvHbznX4Sin75bSnMRcSPM\nDM1ha3w7dwRuoa9x9hM7XdHpqGXQ8TQnBYecEvbb37LX3sdb4Xc56hzlMu/FcgJQTW4kQuTTBUQX\nf4LWJYuUB3+J3vn07TrLnDKeDT5PgdGPCZ6x9RypSDQ9J4/Yqq9OeayH3o2wG2GXvYdsadsqRL2R\n5F80a/EN63DDIcwhw5MdSq2ZBYMIv/sm8XVrMAcOSXY4SXOivt8Ffpn6AO21drUaT1d0MrQOZGgd\nGEh/8o3uPFP+d8JuhOt8V8qMZRW4jkPs6xVEPngXdB3/z6dg9BtwxpMny7V4LjiTlmoLfu6/Wk6y\nmgA9J4/I+2/jhEOovoqOailqgEytM5usLZL8C1GP5FNLNGvRpYswBw+r/DBqzBSPB3PwUKLLlyY7\nlKRZH9/I78seJ11N59EEJP6n003P4/6UO1kdX8eLodnYrp3wfTQlVtF2yh//PeG3Xscz+hxa/Orf\nMPsPPGNC77our4ffosQ5zG2BmzGU5n0Vq6nQOnUBXcfeUXzK4/l6dzZLv38h6pUk/6LZsvbuwS4u\nbJTtPc/EM3w0dnEh9v69yQ6lXjmuwweRj/hb8Hkmesdye+BmfIq3zvaXrWfyUMrdbLO2Mz34InE3\nXmf7aqzsI4cJzpxB+dOPo3XqTIt/+ne8E88/4wJeJyyJfc6K2NfcEbiFVmrLeopW1DVF19Ezs7GK\nf9jyswfF9k7CbjhJkQnR/EjyL5qt2LLF6N3z0dp3SHYoCaN1yEDL7dqsZv8jboTnQjP5OLKYOwK3\ncKF3Ur2U4nTUMng45V72O/t5OvgcETdS5/usLScUJL5lE5GPPyT83lvENqzFCZYndB9uJEL4g3co\ne+y3OGWlpDz4S/zXTkZtcfZEfmt8O2+E3+YG/zVkSRlIk6Pl5P0o+c/RsjAw2BovPMOrhBCJJjX/\nolmqaO/5FYEptyU7lITzjBxD6PXZ+C6+HMXjSXY4dSrR9f3Vla6l8VDKvTxR/jeeKP8bdwduI6A2\njBIyNxLB3rsba/dO7D27sHfvwjlcArqO1qkzisdbcZIYjaC264Cek4ue2xUtJw+1dZtq19mfXNev\n6Ab+n9+M0a+gyuOU2IeZEZrJBM9YhpqDavKWRQOn5+QRXbQQ14pXNiXQFI3uelc2W1vob/ZJcoRC\nNA+S/ItmKfbFMtSWrdDzeyU7lIQz+vRHefsNYqu+wjNidLLDqTMn9++fEvh5nZb5/JTWaiseSrmH\nJ4PTebz8ae5LuZ0Waot6jcGNx7D37sHeswtr93eJ/qFvQVHQMjqidc7EM/68ipawHTJQNK3idY6D\nvX8vdlEhVnEh4ffexC0rQ2nZCj0nr/KP2iHjJ7thWUXbCb89F7vkIN4Jk/CMnXDW8p6TRdwIzwaf\nJ0vL5FLvRbX+foiGSc/KBtvG3r0LPSev8vGeRnc+iX6WvMCEaGYk+RfNjmvbRD9fgnfcxEbd3vNM\nFF3HHDqC6PIlmMNHNblOKT/Vvz9ZUtUUHgjcxTPBGfyp/CnuT7mDNmqbOtmX67rY+/Zi796BvXsX\n1u6dOAf2g+uitmuP1iULz8gx36390BHFMM84lqKq6J26oHfqgmfMOFzXxTlcglW0HXtHEdElnxJ+\n8zUUnw8tO7fyZEDrkomiG6f06zeHDCcw7a4qlfeczHEdZobm4GBza2By0v8vRd1RvD60jp2xiotO\nTf71HrwWfpNDdgnpmixSKERdk+RfNDvxDWtxI2HMwY2/veeZeIaNJvrJR9g7i9Gzc5MdTsLUpH9/\nffGrPu5NuZ3pwRf5U9lT3JdyR8LKkFzXxd69i/jaVcTWrsI9dhS1bRpalyzMgUPRu2RVlPJ4a3f1\nQ1EUtLR0tLR0GDoCAKe0FKu4EGtHIfG1qyvadWoaWqcu2Ht3n7Vf/9l8EPmIbfHtPJr6ID7FV6v4\nRcNXUfe/HTiv8rF0LY00tS2brC2S/AtRDyT5F81OdOlizMHDUHxNN9FQ27RBz+9NdPnSJpP8J7u+\nvyo8ioc7A1N5IfQyfyp/kvsCd9BZ71SjsSpm+PcQX7OK+NpVOEcOo+Xk4R13Lka/gmrPsNeU2qIF\nZv8BmP0HVMQVCWPt3IG1owjPORMw+la9rv+HVsXW8mH0Y+4KTKO9lp7IsEUDpefkEl65AtdxTrny\neqLl51jPqCRGJ0TzIMm/aFasPbuxiwvxX3NDne9rWfQLNllbuNA7iU4/WDW2PnhGjiH44nScS69E\nDaTU+/5rw3VdSpzDFNs7KbZ2UGTtZJ+znz56r6TW91eFoehM9d/Iy6HXeLz8ae5OuY1cPbtKr3Vd\nF2f/PmJrv0v4Sw6hZeVgjh6H2X8AastWdRt8FSheH0aPnhg9etZqnK9jq3k59BqXeS+il9EjQdGJ\nhk7PycMNh3G+3Y+W8f2JcU+9O7NCc7BdG03RkhihEE2fJP+iWYktXYTeoxdau/Z1tg/XdXk/8iEL\nop/QVc/hf8v+yHBzCBd7z69iDFNPAAAgAElEQVTXvuV6j56oqS2JffUF3nHn1tt+ayLmxtll76bY\nqkj2i+2dlLnlpCqp5OpZDDEHkKNfQY6W1ShqwjVF40b/dbwRfpsnyp/ljsCt5Bvdz7i9fWD/9wn/\nwW8rynmGj65I+FvXzb0DyWK7Nm+G3+Oz2DKu8P2McWbTvSld/JjaoiVq2zSs4qJTkv/uRldixCm2\nd9JVbxpXK4VoqCT5F82GU1ZGbPVKArfcXmf7sF2bOeG5rIqt5e7ANHoaPdhmFfJm+F3+q/R/megZ\nx7necXiUum/Bqagq5ohRxJYvrei+0oBubj7qHKuc0S+2d7DbrliUrKOWQa6WxZXmpeRoWbRVq99y\nsqFQFZVrfJfjU7w8E5zBrf6bTmllaB88QGTV18TWrKqYBe3UBXPwcIz+A9DaNs2651KnlBnBWRx0\nSngg5U666nlnf5FocvTv+v17Ro6pfMyn+MjWMtkc3yrJvxB1TJJ/0WzEVixDbd0avZblCmcSdaP8\nPTiLXfZeHkq5my56ZwC66Xk8mvIAq+JreSc8j6WxL7jYez4jzCF1fnnbHDKCyPz3sIq2Y3Q988xz\nXSt1SlkZW0ORvYNiayfH3OP4FT85Whb9jD5c5r2YLL1LvZwU1SdFUfiZ70J8ipcZoZncUHoBA9ZZ\nlK1fS2z3TtSMTpgDB2P0G4CW3vDuYUikImsHM4IzaaO25tepD8nqvc2YlpNH5KMPfvR4T6MHG+Ib\nuYQLkhCVEM2HJP+iWahs7znhvDqZAS9zynkmOIOQG+aRlPtI09qe8ryqqAw2B9Df6MPi6FLeDr/H\nougSrvBdQi89v85mt9XUVPQePYmvWZnU5H9maA4H7UPkG925xHsBOXoW7dT0RjurX1UVNfx7GbUu\niBv38Mqo9ymNBLhg8EScybfgtG2LhUXEtYg7x4m7ceLEibvWd3/HiWOd9HjFcwrQw+hGhtqhwX8P\nXddlSexz5obfYaQ5jKt8l6Ir8tHTnOm5ebjHjuIcPXJKWVtPvTvzIgsod4KkqIEkRihE0ya/gUWz\nEF+/BjcaxRw8LOFjl9iHeTI4Hb/i4xcp95GqnvnmWkMxONc7nuHmUOZHFvJs8Hm66rlc4f0ZXWrY\nFeZszIJBhN9+A/eKaysXd6pP5U6Qrdb2pJd5WHt3E/ngPdT0dPSsHPTMHJTWrROePLuui71rJ/H1\na4ivX4NzuAStcyZj+o2hhWPw8sgPWKguJOZ8gHXc+smxDAwMRf/ubwMDHUMxiLtx5kbeoZ2aToHR\nl/5GXzK1zg3uRCDmxpkTeoPV8bXc4L+GYebgZIckGgA1rR1KIAWruBDzpOQ/U+uCT/GyxdrGILMg\niREK0bRJ8i+ahejSxZhDhqN4E9vec5e1h6eDz5GpdWFq4MYql62kqAGu9l/GWM9I3onM47HyPzPE\nGMjPfBfSWk1sRxejd19Cr7+CtXUzRs/674u/Nr6eFCVArpZT7/s+wTl2lOCMp9EyOuEcLiG86mvc\nYDlKagv0rGy0zJyKvztnoniqX3rkOg52cRGx7xJ+t/Q4WnYu5qixmH0KUNtUJDjDgEy3FzF/nFgw\niuqomBjo3yX4pmKgf5fo62g/mcwfto+wJr6etfH1LIh+QhulNQVmXwqMfmRrmUm/MbrEPsxzoZmE\n3RC/SLm/zk5uReOjKEpl3b85cEjl46qi0kPvxmZrqyT/QtQhSf5Fk2ft3om9owj/dTcmdNxN8S08\nF5zJQLM/1/uuqlH9fjstndsCN1Nk7eAf390UPN4zlvO8ExLWzlLx+jB69ia2dlVSkv/V8XUUGH2T\nloy6kTDlM55Ga59BYOpdKJpWuZKtvWsH1s5i4utWE5n/LgBah45oWdnomdloWTmoaemnLRVzbRtr\n+9aKGf4N63BDQfS8bngnno/Rp98Z+/B31jvSOtCao7Gj2LZd4/fVVmvDRO0cJnrP4ZhznLXxDayN\nr+PT6BJaKKn0N/pQYPQjT8+p99aJG+NbeCH0EllaJvcF7pASDvEjWm4esS+X/+jx873notCwrmAJ\n0dRI8i+avOjSxej5vRN6Q+WXsZW8FHqVSZ4JXOw9v9blFrl6No+k3Mea+Drejszj89gKLvROYrQ5\nPCGJm1EwiNDrr+DGr0cxjFqPV1UnSn7OT5lYb/s8mWvbBGfOAMfBP2VaZdnTySvZnph5dOMx7D27\nsXbuwN5ZTPiDd3FLj6P4/GiZ2RVXBrKywbKJrV+D9c163FgMvXsPvBdditG7b9LWU2iltuQczyjO\n8YyizClnffwb1sTX8WRwOj7FS1+jNwVGX3ro3eq03t5xHRZEP2FeZAHnecZzsff8pF+BEA2Tnp1H\n5J1/4ISCqP7vTw47ax2TGJUQzYMk/6JJc8pKia9ZRWDqHQkZz3VdFkY/5d3IfK71XcFoz4iEjAsV\nCekAsz99jN4sjX7O+5H5LI4u5Rb/ZDK/6xxUU0bPPuA4xLdsxOzTP0ERn926+AYCSoC8JJT8uK5L\n+B+vYu/fS8r9j6L6/D+5vWKY6Dl56Dnf35fgHDtacTKwq5j41s1EPlkAioKR3xvfFddg9OzT4FaK\nTlVTGOkZxkjPMEJOmA3WRtbE1jE9+CIGOn2MXhQY/ehp9MBUEnciGHbDzArOYatVyG2BKfQz+pz9\nRaLZ0jp1BtPE3lGE2qtvssMRolmR5F80aZHlS1HbtEXvll/rsRzXYW74HT6PfVGnyY2h6Iz3jmWY\nOZiXQq/yWvhNHkm5r1ZXFxTTxOjdj/ialfWa/K+Kr01ayU900UJiq74i5e6H0Nq0PfsLTkNt1Rqz\nVWvoPwAA17LAdev16klt+FUfQ81BDDUHEXWjfBPfxJr4el4MvYKFRYbank5aRzprneisdaSz1hG/\n+tMnSaez3z7A34IvoKHxy9QHaa+l18G7EU2JomnoWTlYRYUYkvwLUa8k+RdNlmtZRJZ9hnfipFq3\n94y7cWaGZrPF2s79KXeRq2cnJsif4Ff9XOm7lP8q+39sswrpbnSt1XhmwSCCLz2PG41W66bWcNgF\n16a6ZbgnSn4mBSZUM9Lai61dReSDd/FPmYaemZWwcRW98f7K9CgeBpoFDDQLiLlxdtq72GPvY4+9\nlxWxr3nLOYCDQ1u1NZ20TnTROn73dydaKS3PePK5KraGl0Kv0dvI50b/dU1urQZRd7TsXKxtW5Id\nhhDNTuP9JBPiLMq/XgGxKOag2rX3DDlh/hZ8nsPOEX6Rch8dtPpbjClNa8sgo4AF0U9qnfzrPfJR\ndI34xvWYA05tuWjbLodLHA5963DwoE3JQYeD39ocOuhQXuaSmlrGv/4uFU2r+hnAiZKf+l6t09pR\nRGj2TLyXXF6vVzkaE1Mx6Kbn0e2k1qtx1+KA/S177L3ssfexOb6NjyOLiRAloPjprHX67ipBR7po\nnUhT2/JuZD6Loku4zHsREzznNLhWo6Jh03O7Ev1kAW48hmKYyQ5HiGZDkn/RZB37aD6eoSNRvDXv\nmnPUOcZT5dNRUXkk9f6krEp6nnc8/132f+yy9tSu9l/TiXQfyq4luwkG+3LwoMOh7xL8I4cdHAc8\nXkhvp9GuvUq3fINRY1VatVZ5+vFyigstunaverlLMrr82CWHCD7/N8yhI/GMGV9v+20KDEWni97p\nlJacjutw2DnCHnsvu+197LX38nVsNaVuKQoKAcXPfYE7an1iKponPTMbXBd71y70PDmGhKgvkvyL\nJim+cwfRou20qkV7z/32AZ4sn047LY3bA7fgU5JzY2dHLYM+ei8WRD/mNv3mar3WcVze+UeYHYU2\nhw7ZRCPnoWLT9miE9PYa7dpr9O5nkN5OI729SmqqctrZ2x49vWxYF69y8h90QmyxtnFeoP4ScCcU\nJDjjGbTMLHyXXSWz0AmgKirpWhrpWhoD+P4qSqlTxj57Px21DrRQWyQxQtGYKR4PWqcuWDsKJfkX\noh5J8i+apMhnn+DvNwAtvV2Neqkfc47zx/In6aX34Eb/9Rh12B6xKs73TuSP5U9wwD5YrbKjzRst\nvlga44JLvKS395KeBvrT/07KRZdiDhle5XEKBvr44L1jXHaVW6Wkur5LflwrTvCF6SiGQeDGqUlZ\nybg5aaGm0kJNTXYYognQc3KxigohOd2AhWiWpAGzaHJi69cQW7OS1pdcVuMxPosuo7XSipv9NyQ9\n8QfI0bPoqueyMPpptV63YlmUfgMMxp3rpXdfg3YZBt7+/YmtWVWtcfoN8HL0iMu+vVU7kVodX0uB\n0adeSn5c1yX0+mycwyUEpt1VoxV6hRDJoeV0xdpZhOs4yQ5FiGZDkn/RpNiHDhJ69SX8F16Kr1uP\nGo0Rc2MsjX3BeM+YBrVA0STPBL6MreSoc6xK2x8/5rBxg8XwUacmw2bBIKxtm3GC5VXed+s2Ol2y\nNDasjZ9126ATYrO1jQFG/dxsG1kwj/iGtaRMuwu1Zat62acQIjH07FyIRLD370t2KEI0G8mf0hQi\nQdxYjODMGei5XfFOOK/G43wZW4WKwmBzQAKjq718vTudtAw+jizmav/Zr2p8uTxGejuVnLxTS2C0\nnFyUlFTi69bgGTG6yvvv089g7eo451/809utj39DQPHXS8lP7OsVRD/+kMDUu9A61m4hNCEas/17\nbQq3WxUdeRVQTvwBTsxhnHgMlO+fV75/TtdVho6ofplkbaipqajp7bCLC9E7yc+wEPVBkn/RJJxY\nzZVoBP/1U2rc1991XRZFP2O0OQIjgaufJoKiKEzyTGBmaA7neyeSqqaccVvHcVnxeZSx4z0/qtFX\nVBWzYCDxNSurlfz37mfwwbsRjhy2adP2zDX1q+Nr6V8PXX7i27cSev0VfFdci5Hfq073JURDFQ45\nfPh+hGWfxUhvr6Jp4DrgArjguj/42q14XcXX7in/jkbho/kxbr/XT2o93tKh5+Rh7SjEM/qc+tup\nEM2YJP+iSYh9uZzYmpWk3PcIqr/6K5SesNnayiHnMGM8IxMYXeL0N/rSWp3P4uhSLvFdcMbttmyy\nKCtzGTT09L2zjf6DiC5ZhHP8WJVLZdp3UElLV/lmncWY8adP/kNOmM3WNu4J3FalMWvKPniA0IvT\n8YweV60TGCGaCsdxWfllnPffDuPzK9x+T4DuPWs3YRGPKbwwPcxf/1DKHfcFSEuvnxvntZw8Ih+8\ni+tWraGAEKJ2Gk5BsxA1ZO3dTfjN1/Bdfg165y61GuvT6BIGGQW0bKDtC1VF5TzPeBbHlhF2I2fc\nbsWyGP0KDAIpp/8R1zKzUFu1Ib5uTZX3rSgKvfsabFh/5rr/ddYGfIq3Tkt+nLIygs89jd61B96L\na35TtxCN1b49Nk/9uZx/vBZi7AQPj/wmtdaJP4DXp/LIbzrSIUPjyT+Vs7+KN/jXlp6Th1t6HOfI\n4XrZnxDNnST/olFzwiFCM2dgFAzCHFa72foD9kE2WpsZ5xmToOjqxhBzIB5MlkWXn/b5iht94wwf\nfeauN4qiYAwYSGzNymrtu09/g+LtFsHy03fmWB1bR3+jL5pSNzOGbjxG8IVnUVJS8d9Q8/IuIRqj\ncMjhrTdC/PmxMlq0VPn1v7VgwnledD1xs+WmqTL1rlS6dtd56vFydhZbCRv7TNS2aSipLbCLC+t8\nX0KIBlD2E4/HeeaZZ1i7di1lZWWkpaVx7bXXcs45FbV/O3fu5K9//Ss7duygffv23HHHHfTv/30X\nkWXLlvHiiy9y5MgR8vPzeeCBB2jX7vs+6C+99BLz58/HsixGjRrFXXfdhWFUzJCUl5fz5JNPsmrV\nKnw+H1dccQWXXSYziY2F6ziE5swC08R/5XW1vly8OLqEPC2ndqvo1gNd0ZnoHcdHkU84xzP6R/cm\nfPVFjLbpKrl5P52AmwWDiH7yEfaRw2ht2lZp31k5Gj6/wqZvLAYPO7WkqKLkZ2udlfy4jkNo9izc\nsjJS7n8UxTh9SZMQTY3rVpT4vPdWGJ9P4ba7a1/i81M0TeHnU/y89XqYZ/5azq231+3+FEWpqPsv\nLsQcPKzO9iOEqJD0aTPbtmnTpg2/+93vmDNnDvfeey9PP/00mzdvxrIsfvvb3zJ06FBmz57N9ddf\nz//8z/9w7FhFq8Pdu3fz+OOPc/fdd/Pyyy+TnZ3NY489Vjn2ggULWLx4MX/4wx/429/+xp49e3j5\n5Zcrn3/22WeJx+M8//zz/Md//AdvvPEGK1dWbyZUJE908cdY27cRmDINxaxdIhh0QqyIfc14z9gE\nRVe3RppDsbH5IvbVKY+fuNF3+EjzrCdDakYn1Hbtia+tes9/Va0o/flm3Y9Lf9Zb39RpyU/kg3ex\ntm0mMO1u1Pq8G1GIJDpR4jP31RBjxyeuxOdsVFXhimt9jB3vYcazQdavidXp/k4k/0KIupf05N/r\n9TJ58mQ6dOiAoij06tWLnj17smnTJtavX080GuXqq6/GMAzGjBlDZmYmy5YtA2DRokUMHDiQAQMG\n4PF4uOGGGyguLmbXrl0ALFy4kMsuu4wOHTrQokULrr/+ej7++GMAIpEIy5Yt46abbsLv95Odnc2k\nSZP46KOPkva9EFVnFW4j8sG7+K+bjJbevtbjfR5bQaqaQj+jdwKiq3sexcM4zxgWRhdhu9/X5W7b\nbFFa6v5oVv50FEXBLBhEvJqlP737GWzeFCcec095fHVsbZ2V/Nj79xJdtBD/TVPR2ndI+PhCNDTh\nsFtZ4pPa4rsSn0ledKP+bohVFIULf+bjwp95mfX3EF8uj9bZvrScPJyD3+KUl9XZPoQQFZKe/P9Q\nJBJh+/btZGVlsWvXLrKzs1FPquvNzc1l586dQEVJUE5OTuVzfr+fDh06VD6/a9cucnNzT3nt8ePH\nOXr0KHv37sV1XbKysiqfz8nJqTxxEA2XU3qc4Et/xzP6HMx+te/Fb7s2i6NLGWuOblCLep3NOeYo\nyp0gq+JrKx/7YlmMfv3PfKPvDxkFA7H37sE++G2V99u9R0W14NYt39cCh92Kkp8BRr8qj1MdkQXz\n0Hv0wujes07GF6KhcF2Xr1fEeOy/Stmy0eK2uwNMmRagVevk/W4aN9HLVdf7eGN2mM8+OXOjgdrQ\nMjqCx4u9o7hOxhdCfC/pNf8ncxyHP//5z3Tr1o0BAwawdetWAoHAKdsEAgEOHjwIVJwonO75cDh8\n2udPfB0Oh4lEIvh/0BLy5NeerKSkhJKSksp/q6pKenp6Ld7p6SmKgqbVT2u1xsq1bcpffgEtrR2B\nS69COcP368T3sSrfzzXRdYTdCGN8I9DUxvP9T9VSGesdycLopwzzDqa01OWb9XHufrBFlY8jLaMT\nWqfOWOtWY/7E6l0nH5uaD/J7mWxcb9GvwAvAN9FNeBUv+Z7uCZ/5t/bsJr5hHS0f+nWT+PmozrEp\nzq4p/d7ct8fijTlB9u62OO8iP+Mm1O9M/08dmyPH+PEHNF76eznhcMUVgYS25dQ0jOwc7B1FePs3\nrAUWhWhqGkzy77ouTz31FEeOHOE///M/URQFn89HMBg8ZbtgMIjP5wMqSoZCodApz4dCoVOeP/n1\nJ7b1+XyEw+EfJfonv/Zkc+fOZfr06ZX/vuWWW7jvvvtq8W7PzKxl7XpTV/Lay7gHv6XLf/0veus2\nZ92+RYuzt+xcvOdzxrccQ8e2HRMRYr26MvUyFu1cQrFnF/tWZ5LezmDw0PTqfSiPGEPp55/R6rrJ\nP/m6k4/N4SM15rx0mJYtW6GqCuv3bWRo6mDS2qTV5u2c1r4XnyNQMIj0/gUJHzuZqnJsiqpp7L83\nYzGH12cf5uMPjzNwSID7Hu5E27TkLTJ4pmNz/ERITwvxlz/ux3FMJt+chqom7gTA7d2X4JpVtG7d\nOmFjCiF+rEEk/67r8swzz1BcXMxvf/vbygQ8MzOTuXPn4jhOZelPcXExY8dW3JSZlZVFUVFR5Tjh\ncJgDBw5UlvJkZmZSXFxMr14Vq38WFRXRsmVLWrduXbmPXbt2kZmZWTn2ia9PdtVVV1V2H4KKmf+j\nR48m+ttAIBD40cmO+F5s/VrK5r9PizvvpwwFfuL/QNM0WrRoQWlpKbZ95l7VxfGdbI8UcqP32jr5\nP60Pw80hvPHtW0QXTmH0Od7KG+Krys7vRfyN2ZRs/Aa9Y6fTbvPDYzMrxyFYbrN65SEyci3WhtZz\nT+rtCf8eWrt2EFq7ipaP/HOj/f/5oaoem6JqGvvvTdt2ef7ZMvbusbn93lTye5lA+U/9eqszVTk2\nO2XCXfenMv3JUo4dDfPzKSloWmJOAKyMTkTfeoMjBw6geM7cqrg25MRCiAaS/D/77LNs2bKF3/3u\nd6eU4vTt2xfTNPnHP/7BZZddxooVK9i5cyejRo0CYNy4cTzyyCOsWbOGXr168corr5CdnV2ZwE+c\nOJG5c+cyaNAgAoEAc+bMYeLEiUDFVYFRo0Yxa9YsHn74YQ4dOsSCBQt48MEHfxRfWloaaWnfz2iW\nlJTUyYe267qSDJyBXXKIsldexDvpItS8blX+Ptm2/ZPbfhxeTG+9J21p02i/9xPMc/jthtfwHrMZ\nOESv/vto1RotM5vIqi/xtb/0tJv88Nj0+iAnT2fdmijfdtqIR/GQp+Yk/HsYnPcuRt8ClA4Zjfb/\n50zOdmyKqmnMvzcdx+XVl0Ls2mFx7y9SSEvXGsR7OduxmZmtcvdDKUx/opy/P1PKTVMDGGbtTwCU\nTl1AUYjuKMLo2r3W4wkhTi/pyf/BgweZN28ehmEwderUysevvvpqrr32Wv71X/+VJ554gjlz5tCu\nXTt+85vf0KpVKwC6dOnCAw88wJNPPsnRo0fp0aMHv/rVryrHmDRpEocOHeKRRx7Btm1GjhzJ5MmT\nK5+/8847eeKJJ7jlllvw+XxcddVVDBo0qP7evKgSNx4jNHMGenYungmTEjbuUecYq+NruTdwe8LG\nTIZ0LY20leOxe+8mJbVms1pGwSBiyxbjveBnVS4Z6tPPYOniKHvPrZuFvawdRVhbN5H6i98kdFwh\nGgLXdXn3zQjfrI9zz4OppKU3rvsWOnbSuPcXKTz713KmP1XO1DtT8PpqdwKgmCZa5y7YxYWS/AtR\nhxTXdd2zbyZOdvLNv4mUmppKWZm0Ofuh0GsvE9+2hdSHf43qD5z9BVRcvm7dujVHjx494wzWO+F5\nbIhv4jepv0jsjWv1rLTU4Xf/epzIzS/zy/6X06UGi5Q5x49R+rt/I+X+R9Ezs370/OmOzSOHbf77\n38uw7n+OO3N/Rk+jR43fw+mUP/tXlEAKgRtvTei4yVaVY1NUXWP9vblwfoSPP4xwx30p5OQlfR4O\nqNmxefyYw7NPlGOaCrffE6hyp7EzCb/3Fm44hP+aG2o1zpmcfBVfiOaq8fQ1FM1S9MvlxFZ+RWDK\ntCon/lURc2MsjX3BeM+YRp34A3z9RYw2bTV6dvOxIPpJjcZQW7ZCy8mrVs//Nm01WnWMYmzOp7ve\ntUb7PROrcDvW9q14J12Y0HGFaAg+XxJlwbwIU24LNJjEv6ZatlK596EUAJ78cznHjjq1Gs970aV1\nlvgLISpI8i8aLHvfHsL/eA3fZVehd/nxbHRtfBlbhYrCYLNxt5SrWNE3xrCRJuf7J7Imvp5v7UM1\nGsssGERs7Spcp+of3mrPInybE1vy47ou4Q/fwxg4BK2dLOglmpbVX8d46/Uw19/kp2fv5HX0SaRA\nispd96eQmqry5J/KKC+r+QmAokpaIkRdk58y0SC54TDBmTMw+hVgjhid2LFdl0XRzxhtjsBQGveH\n7/atFseOOgwebpKrZ5On5bAw+mmNxjL6FeCWlWLvrNoiO2E3wsH85YR2t+T4sdrN9p3M2r61otf3\neTLrL5qWzRvjzJkV4tIrfQwc0rjbk/6Q16dw2z0BfH6Vt9/48Xo5QoiGQ5J/0eC4rkvo1Vmg6fiv\nuj7hZTmbra0ccg4zxjMyoeMmwxfLYvTuZ5CaWvGjPMk7gS9jKznqVK/dJ4CakoretTux1VUr/dkQ\n34iRcYzWbRS+WR+v9v5Ox3VdIh++jzl4GFpa4hfSEyJZdhRZvPhckAmTPIweVzdtLJPNMBSum+xj\n7ep4wn4nCCEST5J/0eDEPl9CfNsWAjdPq5Nez59GlzDIKKCl2rgXWSorddiwNs6I0d/PIPbUe5Ch\ndeCT6OIajWkUDCK+bjVuFW72Wx1fR4HZh979DDasTcwHvbV1E/auHXgmnp+Q8YRoCPbvtZnxdJCh\nw00mXeRNdjh1qlMXnXHnepg7J0Q4lLgrgkKIxJHkXzQobjxGZOF8vOdfXCf13gfsg2y0NjPOMybh\nY9e3r1fEaN1GJa/b9zcMKorCJM8ElkW/oNyp/sJHRt/+uOEQVuG2n9wu4kbYGN/MAKMfffoZFG6z\nCIdr1zjMdV0i89/HHDoCra105BBNw+ESm+lPlpPfW+eyq32NvsFAVZx3oRevV+G9tyLJDkUIcRqS\n/IsGJfblcnAcPMMTW+d/wuLoEvK0HDJr0A6zIam80XeUiaqemkwUGH1ppbZicXRptcdVfX70/F5n\n7fqzIb4JUzHooXcjJ0/H9Chs/qZ2s//Wpg3Y+/bilVl/0USUljr87YkgHbtoXH+T/0c/q02VYShc\nc4OfL5fH2LZFyn+EaGgk+RcNhmtZRD5diOeciShm4m+GCzohVsS+ZrxnbMLHrm+F2yyOHHYYMuzH\n3ydVUTnXM47FsaVE3OrPvJkFg4ivX4NrWWfcZnV8Lf2MPmiKhqYp9Oqj8826mn/IV9b6DxuJ2rpN\njccRoqEIhxymP1lOi5YKU6YF0LTmkfifkJOnM2qsyeuvhIlGZTkhIRoSSf5FgxFb+SVEo3hG1k1J\nzuexFaSqKfQzetfJ+PVpxbIYffobpLY4/Y/wUHMQJiZLo19Ue2yjV19cy8Lauum0z0fdaGXJzwl9\n+hls2hjHitfsQz6+YR32twfwTkzcCs5CJEss5jLjmYqyu6l3BjDN5pX4n3DhpT5cYP57Uv4jREMi\nyb9oEFzbJvrJAjxjxsPtD20AACAASURBVKN4E39DnO3aLI4u5RxzDKrSuA/78jKH9WvjDB955qsj\nuvL/s3ff4VGV2QPHv9N7SCCEDqEKkd6b9CpKV0Ck/HBBRFQssK7KLgqouIK6VhQXlI4UQUUIHUGk\ndxGREnoJJKRNn/v7A8kaCCEJk8xkcj7PM4/M3Hvfee94M3Pue889r5b2htasd27Grdx5BD8zKoMB\nXUwtXPv2ZLr8oPtXtH+m/NxUrYYOr+fGFYmcUnw+HLE/YGj2AOoi4TneXohg4vUqfP1lKslJCsOf\ntmIyF+zvm3thMKh4pL+JLRudnDqR8+8GIUTeKLzfSiKouPftxpeagv6B1nnS/n73QeyKg2aGRnnS\nfn7atd1FkXA1Ve7LembQ5oYmePGw3bUrx++hq1sf9+EDKG7Xbcv2ug9QW3c/WtX/3t9gUFG1upZD\nuUj9cR/chy/+Coa2HXO8rRDBxOdTWDA7jfNnvIwYbSHsDlfmCpNqNXQ0aqpn0by0XF8ZFEL4l3wz\niYBTfD4ca1djaNEKtcmcJ++xwfkTzfSNMKlMedJ+flEUhV+23pjR9243DxpUBtoYWrLWuQFPDkf/\nddVjABXuI4czvH4j5ecI9XV1btumZm0dhw+48fmy/wOv+Hw4Vq/E0LwV6rCCXXpVFG6KorB8sZ3f\nDnsY/rSVYpH+m/W6oHu4lxFHmsKaVZL+I0QwkOBfBJz7wD58iQkYWrXLk/ZPeeI45T1Na0PeVBDK\nT+k3+jbL3g3RrfQtcSkuJie/y37XIRQle4G5SqdHV7M27ltSfw65j9yW8nNTTE0dyckKZ0/ffY6A\nm9z7duNLvIahbYdsbyNEMIpd6WDHLy6eeMpCqTIS+P+Vyaymd38zG9Y4OXdW0n+ECDQJ/kVAKT4f\njnWrMDRvidpizZP32ODcQk1tDMU1Bb92/C9bXdxfS5ftdAKL2sxrtnHU1t3PzLTZfJDyKXGeM9na\nVl+3Ae5fD6E4/jdat9e9n9rajCk/N9nC1FSoqMl26o/i9eKIXYmhZRvUVlu2thEi2Ph8ChvXOVgf\n62To3yxEV8o6Ha+wqllbR+26OhbNteP1SvqPEIEkwb8IKM+vh/BduYyhdd6M/CZ4E9nr3k/bEJjU\nK/1G3xY5K4NqVpvoZXqY12zjCFOH8W7Kf/gqdR7XfNey3E5b9T5Ueh3uXw8CN1J+Drt/o56+9h23\nub+2LtvBv3vPTnzJyRjatM/+zggRJNwuhW1bnPx7UjKrvnMwYLCZ+2J0ge5WUOvxiImEaz42rXMG\nuitCFGoS/IuAURQFx9ofb9R2z6N8703OrZRSl6CqtnKetJ+fdu1wERZ248ba3IjUFGOY5XFesI7m\nqu8abyS9w3L7D9gVe6brq7RadLXq4tp7Y8Kvw+4jaNFQXVvtju9Rs7aOyxd9XLmUdeqP4vXiWLsK\nQ6u2qM2WXO2PEIGQnOxj9Q92Jv0ziVXfO6jbQMerE8Oo28D/c5OEGptNTc++JmJXOrh8l+8IIUTe\nkeuTImA8R4/gvXAey9ARedK+0+dki+NnehofQqUq2HW2FUVh+1YXTZob7nmW0IraCjxvfZp97oMs\nd/zANtcOuho70VLfFI0qY66yrm4DUmd8gi81lb3uA9TSZZ7yc1PxKA0lSqo5dNBN2xJ3znt27dqO\nkpaKoVXbe9oXIfLL5YteNq13snuHi4iiaro+ZKRBYz26QlrDP7fqNdSxd5eWRXPTGDXGWmhmPRYi\nmEjwLwLixqj/KvQNm6AOj8iT9/gp+WdUqGmor5cn7eenE394uRrvo1FT/4wuqlQq6ulrU0sXw0/O\nn/nBsYrNzi30MD1ELW1M+smStnJVVGYzCfu3c6jcEYZZBt217fv/rPrTtkPm8zUoHg+ONT9iaN0+\nz6o7CeEPiqJw/JiHTeucHDnsoVIVDYOesFDjfq0ErbmkUqno09/Mu5OT+Hmzi5ZtDIHukhCFjgT/\nIiA8x4/hPX0K84DBedK+T/GxMjGWVsbm6FQFPw/3l61OYmrpKBLu30w9rUpLW2Mrmugbssq5li9T\nv6aSNppexocpry2LSq1GV7see07GoimrzjLl56aatXVsWOMkOcmX6QzErh3bwOXE0DJv5nQQ4l55\nvQr797jZtN7JhXNeatfT8dxYK+UqyE+mP4RHqHmop4kVS+3E1NJStJhURxIiP0nOvwgI55pV6Oo1\nRFMsbyrw/Ob+nUvuy7QytsiT9vNTaoqPA/tyfqNvTpjVZnqbujPeNg6bysq/Uz7486bgBAwtW7O/\nXCrVD6bh+OgDnFs24UtOvmNbZctrsIWp+PXQ7Tf+Km43jnWrMbTpgMpYsOdcEKHHbr9RueetCUks\nWZhG5apaXp4QxuP/Z5HA38+atNBTLlrL4vn2bJcgFreLjo5m9OjROdpm48aNqFQqdu3KegLICRMm\nYLXmTRU+EVjybSbynefkcTwnjmEb+5rf2nQpbi57L3PBd4mL3kvscx+kha0pRdRheL0F+8ay3X/e\n6Fstlzf65sSNm4IHccJzimX275iYNIU21gc4WgmGOHqhTU7AsXEt9hVL0Fa9D329huhq1kFl/F+K\nj1qt4v5aOg7td9OkecZL+q7tW8HjwdBCRv1F8Ei45uOnjU62/+zEaFTxQBsDTVoYMJkktSevqFQq\nHhlgYuqbyez8xUXjZpL+kxvLli0jIiJvUmdF6JLgX+Q7x9rV6OrUQxNVIsfbOhUnF72XufhnkH/B\ne4mLvktc9V1DQaGIKoySmhLE6O/jkWK94c4D1AXC/2b0vfcbfXOikjaaF6yj2es+wArHSrRoiYlq\njq6bFmPXh/HGncS1Zxf2FUtJW7wA3f210NdriLZ6DVRaHTVr65j5eSpOp4LBcKPfisuFY10shrYd\nURnkh14EnsOh8P0yOzu2uShVWkOffmbq1Neh0UjQnx8ii2vo+rCR75Y6qB6jI6yIJCNkl91ux2Qy\nUa9ewb+nTeQ/+UsT+cpzJg7P0V8xtu+c5Xp2xc5JTxzbnDtYav+OT1Jm8M/rk3nx+qv8O+UDvrev\n4pz3PFGaSDoZ2vG89WneCZvI5CL/5Bnrkzxq6U1RbcEfDTl53Ev8Ff/d6JsTKpWK+vo6vGoby+SS\n/0T3Z5UflVqNtmJlzH36EfavN7EMfgLUalLnziLp9VdJ+2Ye5TUn0Wrh6K//S/1xbvsJAEPzgj/n\ngij4/vjdzdS3kvnjdw9/G2VhzN+t1G+kl8A/n7VsY6B4CTVLF4Ze+s+sWbPQarVcunQpw+vXrl1D\nr9czffp0tm3bRvfu3SldujQWi4W6desye/bsDOvfTNP54Ycf6Nu3L2FhYTzyyCPA7Wk/2WnvpsuX\nL9O7d28sFgulSpXizTffvOs+JSYmMmrUKEqVKoXBYKBBgwbExsbm9KMRASYj/yJfOdauRlezNppS\nZe64zg/21fzoXIMKFcXURSmpjqK0piT19XUopS5BCU0URlXmlWRCzS9bndSoqfX7jb45oVNpsWlt\nJGdyGUWl0aCLqYkupiaK04n714O49uzE8+VHVFIeY//KcGKKW9AUj8K5fg3GDl1Q6aUeuggct0th\n5XcOtmx00qKVngd7mNBLuc6AUatVPDrQzHtvJ7N/r5u69UPn+6FXr16MHDmSb775JkOAvmTJEgAe\neeQRYmNjadGiBSNHjsRoNLJ161aeeOIJfD4fQ4YMydDeiBEjePzxx1m2bBkaTeY3ScfFxeWovQED\nBrB06VLWrl3Lq6++StGiRRk5cmSmbbtcLjp27MilS5eYPHkyZcqUYc6cOXTr1o09e/ZQq1ate/m4\nRD6S4F/kG+/5c3gOH8A6Ztwd17novcRq5zoGmwdQV1cLvSp0fghyyulUOLjfzcAhBaMcpspgQF+v\nIfp6DfGlplBr+QmW/xLB9fcmo7WaQaNB37Tg34AtCq4zcR7mf52Gy6UwYrSFqvcV/EpgoaBkKQ0d\nuhhZtshO1WpaLNbQSEooUqQIDz74IPPnz88Q/M+fP59OnTpRtGhR+vfvn/66oii0atWKs2fPMn36\n9NuC9e7duzNlypQs3zMn7bVr145///vfAHTu3JlLly4xadIkRowYgVp9+/+DuXPnsm/fPvbv309M\nTEz6dseOHWPixIksWrQom5+MCLTQ+AsTBYJj3Wq01WPQli2f6XJFUVhiX0F1bTUa6xsU6sAf4PAB\nN1qNiuoxBS9AUVus1OxVC7fKwNXHJmBo0wFz/8dR6QrevoiCz+tVWP2DnQ+nplA+WsuLr4RJ4B9k\n2nUyEFZExfIlmc84XlANGDCAbdu2cfr0aQAuXLjApk2bGDBgAAAJCQk8++yzVKhQAZ1Oh06n4/PP\nP+f333+/ra1u3brd9f1y0l6vXr0yPO/bty/nzp3j7NmzmbYdGxtLrVq1qFatGh6PJ/3RsWNHdu7c\nede+ieAhwb/IF97LF3Ef2IuxQ5c7rnPI8ytHPcfoY+qejz0LXnt3uahdT4dWVzBTEkwmFZWravn1\nhBFj2w7oqtUIdJdEIXTxgpf/vJvCti0uBv/NQv9BZqniE4Q0GhX9BprZt9vNkcO3lwkuqB566CEs\nFgsLFiwAYNGiRRiNRnr27AnA0KFDmT9/Pi+99BKxsbHs3LmTYcOG4XA4bmurRIm7F8nISXtRUVGZ\ntn/hwoVM246Pj2fv3r3pJxU3H5MmTeLMmTN37ZsIHpL2I/KFY10s2spV0UZXynS5W/GwxL6CtoYH\nKKGJynSdwiQ1xcfRIx5GjLYEuiv3pGYdHetjHfToq6TPGixEfvD5FDavd7Lqewc17tcxfJQFq03G\nu4JZ2fJauvUwog2hyMRkMtGzZ08WLFjAuHHjWLBgAQ8//DAWiwWHw8H333/PtGnTeOaZZ9K38fl8\nmbZ1t+/QnLZ3+fLlDM9v3phcqlSpTNcvWrQotWvX5ssvv8yyHyL4hdCfmAhW3vgruPfuwjLizhOR\nbHBuxqm46GLsmI89C17797qxhamoVKVg/4neX0vH0oV2zp/1UqZcwd4XUXBcjfeyYHYaF8/7eHSg\nmXoNdXLyWUC0bh96xRwGDBhAt27dWL16Nb/88gsvv/wyAE6nE5/Ph/4vRRCSk5NZsWJFrt4np+0t\nW7YsQ+rP4sWLKV26NGXLls10/Q4dOrBy5UpKly5N6dKlc9VHERzk11jkOeeGNWjKR6OtXDXT5Ym+\n66xyrOURU09MhaSKz93s3eWibn19vtb2zwtFwtWUq6Dh0AG3BP8iz92cF+O7pXaiK2l58RUb4REy\n2i8Cq2PHjhQrVoxhw4YRHh5O165dgRs3BDdq1Ii3336b4sWLo9VqefvttylSpMhto/LZkdP21q9f\nz9ixY+nYsSNr1qxh9uzZfPzxx5ne7AswePBgpk+fTps2bXjppZeoVq0aiYmJ7N27F5fLxVtvvZXj\nPovAkG9Fkad8Cddw7dp+o8TjHUbeVthXUkpTgib6hvncu+B07ZqPk8e91G8UGjck1qyt4/CB0Mnh\nFcHpeqKPGZ+msmKpnYd6mRj+tEUCfxEUdDodffv25fz58/Tp0yfDyPy8efOoUqUKQ4YM4dlnn6Vv\n374MHjw41++Vk/amT5/O77//Tq9evZg9ezYTJ05k1KhRd2zbYDCwfv16HnroISZPnkynTp0YNWoU\nu3btomXLlrnus8h/KiXUZtXIB/Hx8XnSrs1mIzm5gE9Je4u0pYvwnj6F9bmxmQb/Jz1xTEv5iBes\no6moreC399VoNERERJCQkIDX6/Vbu/lhfayDXdtdjH3NFjSpCvdybF684OXdycn8Y4KNYpGZ16Yu\nTArysRmMrFYrmzdcY9kiOyVKqek/yExkcTnOcqMwHJuRkZGB7oIQASfX4UWe8SVdx7XjZyyPD8s0\niPUpPr6xL6ORrr5fA/+Cbu8uF/Ua6oMm8L9XJUqqiSyu5vABN63aSVAm/Cct1ce8r66yf6+dLt2M\ntG5vKPCpckIIkdfkmqjIM86N61AXL4E2pmamy7e7dnHJe4UeprvXLi4sLpzzcuG8j3oNQyPlB25U\nqGjUVM/6NU5SUzKvOiFETp2J8/DelGQuX/QwZqyNth2NEvgLIUQ2SPAv8oQvJRnnti0Y23dGlcnN\nQ3bFzgrHSroY21NEHRaAHganvbtclI/WhFzaQuv2BooUUbNkYWhN4CPyn6IobNvi5KP3Uqh6n46/\n/zOKUmVC6+9FCCHykgT/Ik84N29AHRGBrnbdTJf/6FiLUWWkjaFVPvcsePl8Cnt330j5CTVarYr+\ng8wcPuBm325XoLsjCiiXS2HB7DSWL7HTp5+JRwea0evlZ0wIIXJCcv6F3/nS0nBu3Yy596OZjvpf\n9F5mo/MnhluGolPJIXhT3EkviQkKdeuHTsrPX5Uqo6FzNyNLF9qpVEVLWBEJ2kT2Xbnk5asZqbjd\n8MyLVsqUle8OEdzyooCHVqvFZDL5vV1RuMivr/A715aNqK1WdHUb3LZMURSW2pdTXVuVmtoaAehd\n8Nqzy0XV+7TYwkL3z7J1ewPFo9R8Mz8NKTQmsuvgPhfv/zuZYpFqxoyTwF8IIe5F6EYZIiB8aWk4\nf9qIoV0nVJrb83APeY7wm+cYvU09QqaajT94vQr797hDMuXnrzQaFf0GmTl21MOu7ZL+I7Lm9Sp8\nt9TO7P+m0b6zkSHDLZjM8rMlhBD3QoZPhN/4EhNImfEJqogI9A0a37bcrXhYal9BG0NLSmqiAtDD\n4PX7bx5cLoVadUIz5eevokpo6NbdxPLFdqrep5OJmESmrif6mDMzlfjLPkaMtlClWuj/bQghRH6Q\nX13hF94L50n+cCpqiw3bU2NQaW8/r9zo/AmH4qCrsWMAehjc9ux0EVNTh9FUOK6GtGitp3RZDQvn\nSPqPuN0fv7t5f8qNfOkxf7dJ4C+EEH6Uq+C/UqVK7N+/P9Nlhw4dolKlSvfUKVGwuP/4neSP30Nb\nqTKW4U+hyuRmpOu+JFY51tLd+CAmldys9FdOp8KhA27qh1Bt/7tRq1X0e9xM3EkP27ZI+o+4wedT\nWL/GwecfpVK/kZ6Rz1opEi5jVEII4U+5+lY9deoUTqcz02VpaWmcOXPmnjolCg7X3l2kfvEJhqYt\nMA8YgkqbeQC73P4DJTTFaaJvmM89DH6/HnSj1aioHlN4gn+AYpEaHu5t4vtlduKveAPdHRFg9jQf\nX32RyrrVDh7/PzMP9zah0RSOK2FC3Ks2bdrw2WefcerUKVQqFa1aZSyj/fLLLzN06ND059HR0ZhM\nJmw2GxERETRu3Jh3330Xl+t/gzETJkygf//+t71XdHQ0q1atSn9+/PhxHnvsMaKioggLC6NGjRpM\nmDCBuLg4rFZr+kOtVmMymdKfz507l1mzZqHRaDKsZ7Va2blzJwBDhw5Fr9djs9kIDw+nevXqPPfc\nc1y6dClbn4vb7Wby5MlUr14di8VCuXLlePTRRzl48GD6Ojt27KBjx47YbDZsNhsdO3Zkx44dGdpR\nqVT89ttvGV6bNWsWTZs2zfC5hIeHc+3atfTXVq1aRXR0NAD3339/+v7pdDr0en3685EjR2Zrf/wl\n28G/w+Hg2rVrXL16FYCkpCSuXbuW4XH+/Hm+/fZbSpcunWcdFsFBURQcG9eSNv9rTA/3wvRQz0zL\negKc8sSx072HvqaeqFUyinerPbtc1K6nQ6srfIFO0xZ6oitpWTgnDZ9P0n8Kq3NnPbz/TgpX432M\nGWujdr3QvvFdiLx2+PBhfvzxxyzXWbZsGcnJyVy5coVp06Yxf/58unXrlqNUzBMnTtC4cWOKFSvG\nnj17SEpK4scffyQ+Pp7ExERSUlLSH1FRUfz444/pzwcOHAhAo0aNMqyXkpJCo0aN0t/jhRdeIDk5\nmcTERJYvX058fDwNGjTg4sWLd+1fv379WLRoETNnziQxMZHffvuNhx56iO+//x6A7du3065dO7p0\n6cLZs2c5e/YsXbp0oV27dmzfvj3bn8NNGo2GKVOmZLrs8OHD6fvXp08fXnnllfTnn332WY7f615k\nOxKbMmUKxYsXJyoqCpVKRefOnSlevHiGR7ly5ZgyZQp/+9vf8rLPIsAUnw/78iU4Vv2AedATGFq2\nvuO6PsXHN/ZvaairRyVtdP51soBITfFx9FcP9QpRys9fqVQqHh1o5sJ5Lz9tyPxqoghtO7Y5+XBq\nCtGVNDzzko3iJWS2XiHu1csvv8yrr76arUBeq9XSsmVLlixZwtatW1m5cmW232fChAk0btyYDz/8\nkLJlywI3RsA/+ugj6tSpk+v+38l9993HV199RdGiRZk6dWqW665fv56VK1eyYsUKmjVrhk6nw2Kx\nMHjwYP7xj38AMG7cOB5//HFefPFFihQpQpEiRXjxxRcZOHAg48aNy3H/xo0bx8cff8z58+dztX/5\nJdvVfnr27El0dDSKojBs2DBee+01KleunGEdvV5PjRo1qFs381ldRcGnuF2kzfsKz/FjWEc+gzY6\n6/s7drh2c8F7ieGWofnTwQJm/143VpuKSlUKb+Gt8Ag1PfuaWTw/jer36yhRUoK/wsDpVFi+2M7u\nnS569TXRpIVeyv8K4SdPPvkkn3zyCYsWLaJfv37Z2iY6OpoGDRqwefNmunXrlq1tYmNjefPNN++l\nqzmm1Wrp0aMHsbGxWa4XGxtL48aNqVChQqbL09LS2LJlC//6179uW9avXz86duyI3W7P0aRqderU\noUePHkycOJFPP/0029vlt2xHHHXq1Ek/i1OpVHTr1o3IyMg865gIPr7UFFJnfo5y/TrW0S+iiSqR\n5fp2xcFyx0q6GDsQri6ST70sWPbuclGvgR61unAHPQ0a6zi4X8uC2WmMfsEqud4h7o/f3Syaa0el\ngtHPWylXofCe/IrQdemZ4X5v01i3PmXH3H1EWq/X8/rrrzN+/Hj69OmT7fZLly6dIWf9bq5evXrP\nqd67du0iPDw8w2vnzp3DYrHccZvs9PNufUtISMDn82W6TqlSpfD5fFy7do0yZcrcZQ8yeuONN6hd\nuzYvvfRSjrbLT7lKwB4yZIgE/oWM99pVUj56D9xurM/cPfAHWOVYg0Glp62h1V3XLYyuXfNx8riX\neo0KZ8rPX6lUKvr2N3M13sfGtZL+E6qcToWlC9OY/mEqMbW0vPAPmwT+QuSRxx9/HL1ez8yZM7O9\nzfnz5ylatCgAOp0Ot9t92zputxud7sbvVrFixe45xaVhw4YkJiZmeGQV+N/azzu5W98iIiJQq9WZ\nrnPhwgXUajURERHAjasNt34Wf/0c/qpy5coMGTKEf/7zn1n2L5ByFfzb7XZeeeUVqlWrhtlsRqPR\n3PYQocNz7gwpH05FXbQo1qeeQx0WdtdtLnmvsNG5hd6m7uhU8uOemX27XRQvoaZMWfl7AbCFqenT\nz0TsSgfnz0r1n1Dzx+9upr6ZzNEjHkY+Z6VnXzMGg1zhESKvqNVqJk+ezBtvvIHD4bjr+nFxceze\nvTu9UlD58uU5efJkhnXS0tK4dOlSeipNp06dWLx4sf87nwWv18vy5ctvq2h0q06dOrFjxw5Onz6d\n6XKz2Uzz5s1ZtGjRbcsWLlxI8+bNMZvNQOafxYkTJ+6YUjR+/HhWrFjBgQMHsrNL+S5XUdnTTz/N\nvHnzGDBgADExMej1UpkhVLmP/krq11+ir10PU98BqLJ5YrfUvpyq2srU0sbkcQ8Lrr27XNRvKHnO\nf1Wnvp6D+9wsmJ3Ks2NtaLXy2RR0TqfCyuV2fv7JRYtWerp2N0nQLwqFEh9+4fc2tZlMoJmVHj16\n8PbbbzNnzhweeuihTNfxer3s2LGDZ599lmbNmvHggw8C0LVrV5577jlmzJjB4MGDcTqdvPzyyzRs\n2DD9ns8JEybQqFEjxowZw7hx4yhdujSnT59m6tSpPPHEE9SuXfvedvgWx44dY+LEicTHx/PCCy9k\nuW67du3o2rUrPXv25NNPP6V+/fq43W6WLVvGmTNnePnll3nnnXfo0KEDVatWZfjw4SiKwpdffsmc\nOXNYu3ZtelsDBw5k0qRJ1K5dmwoVKrBz506+/PJLZs2alel7lypVitGjR/POO+9gtVr9+RH4Ra6C\n/++++453332X0aNH+7s/Iog4d/6C/Zt5GNp3xtjpwWwHqYfcv3LE8zuv2F6UwPYOLpz3cuGcjyF/\nk5SfW/V61MS7k5NZu8pBl4dkQriC7PgxDwvnpKFSwchnrVSuKlcBhchvb731Fm3btr3t9V69eqHV\natFoNFSpUoVHHnmEMWPGpP9uR0ZGsmrVKsaNG8e4cePQ6/W0atWKxYsXp69TqVIltm/fzvjx46lT\npw5Op5MyZcowYMAAqlSpkq3+7dy587YAec6cOfTs2ROAadOm8fHHH6NSqShVqhSdO3dm9+7dlCpV\n6q5tL1q0iClTpjB48GDOnj1LREQELVu2ZPz48QA0a9aMdevW8dprrzFhwgQAmjRpwrp16zLU8H/1\n1Vfxer20a9eO+Ph4ypcvz9tvv51+opSZcePG5XsJz+xSKTkp6PqnkiVL8vXXX9OpU6e86FPQi4+P\nz5N2bTYbycnJedJ2TiiKgnPtKhxrfsTUpz+GJs2zva1LcfFW8jRq6mLoY+qeh73MmkajISIigoSE\nBLze4EshWbnCzrGjHp4bawt0V7Ilv4/NwwfdfPVFKqNftFI+xHLCg/3Y9Ie/jvY3f0DPgz3ybrQ/\nWL43Q0FhODbz837FvDgutVptjqrPCJGZXOX8P/XUU8yePdvffRFBQPF6sS+ej2P9Giz/92S2A39F\nUTjgPszk5Hfx4aWroWMe97TgUhQlPeVHZO7+WjrqN9KzYHYabpdM/lWQHD/mYdpbyRw57OHJZyz0\nelRy+4UQIpjkakjNbDbz008/0bx5czp06HBbiSaVSsXzzz/vlw6Ke6N4vShOBzicKE47isNx4+F0\nojgdKA47OJ1/vubAe/E8vvh4rE89h7Z85jey3OqKN57F9m854vmddoZWdDF2wKgy5vGeFVynTnhJ\nTFCo00BSfrLSv21lkwAAIABJREFUo6+Jdycnsep7Bw/3lpGuYOd0Kvy4ws7WzS6aPaCnWx6O9gsh\nBMCbb76Z6TwDFSpU4PDhwwHoUcGQq7QftTrrCwYqlSpkLxlC8KX9KA477t+O4D68H9/ly38G9TeC\neTIp04XBgMpgRGU0ZvgvRiNqkxl9i1ZoIovf9X1diotYx3rWOjdQWVuJR0w9Kam5ewnQ/BDMl6+X\nLkwj/oqPEaOD7yagOwlUasXRI25mfJLKqDFWKlYOjfSfYD42c+v4Hx4WzUnDp0C/gSaqVMu/E1tJ\n+/GfUDw2byVpP0LkcuTf5/P5ux8Fil6vx2Aw+L1drVaLzZa9HHBv0nXs+/di37cbx2+/otLpMdWq\ng775A6hMJtQmE2qjEZXRhNpouvHazWD/Lidvd6MoCnvs+5mXuAgfPp4s9gSNTPWD6ubem32xWCzZ\nmt48v3g9Cgf2JdGnXzg2W9Z1jINJTo5Nf2rYGI7+Cgvn2Bk/qSRG470du8EgWI/N3HA6fXz7zXU2\nrE2hVVsLvfuF5/v/o0Adm6EolI5NIcSdhcZQWj5zuVy4XC6/t3u3ESxv/BXch/bjPnQAb9xJVLYw\ndDVrYxn2JNpKVVH9WQJMAbx/PjLweMGTek99vOy9wmL7txz1/EE7Q2u6GNtj8BpISUm5p3b9TaPR\noNfrSU1NDaoRrCOH3TjsPqre5y1Qo5WBHF3t/JCWQwcUFs65Qu9+5oD0wZ+C9djMqVMnPMz/Og2f\nT2HEaAtV79PhdqdmerExL8nIv/+EyrGZlbwYuBOioMlV8L958+a7rnO3yRfE3SmKgvfc2T8D/v34\nLl5AHVUCXc06mLr3RlO2/D2P4meXU3Gy2rGe9c6NVNFW5hXbi5TQROXLe4eSvbtcxNTSYTQFz1WS\nYGcwqOg3yMxnH6RQrYaOmrXlXolA27PTxcI5aTRqquehXiaMRjmehbhVZrO/3qu7pV0LkR25Cv7b\ntGmDSqXKcFnw1pSPUB01yGuK14vn5PH0EX4lMQFN+Wj09Rujq1kLTVTJ/O2PorDPfZCl9hUADDU/\nTh1dzaBK8SkonE6FQ/vdDBhS8Eev81vlKlq6PGRk7sxUnnzGSnQluWgZCIqisHGtkx+/c9C9j4mW\nrWUUVYg7MRql8IUITrn6Bd27d+9tryUkJLB69WqWLFnC9OnT77ljhVHikoWkbNmI4nCgrVINY7tO\n6O6vhbpI+F239Sk+TnlPk6qkUlQdQVF1BCbVvd0UdMl7mW/s3/KH5zjtDG3obGyHQSU/9rn160E3\nag3UiJGR69xo18nA9es+/vtZKqNfsBJVMnuzTQv/8PkUvl1sZ8c2F4OeMFOrjpSqFUKIgihXwX+d\nOnUyfb1NmzaYzWamT5+e6WxyImvaEiUw9XoUbfUY1Ka7jw4risIZ7zl2u/eyx7WfROU6BvQ4cAJg\nwkhRdQQRf54M3PqwqayZjuA7FSerHGtZ79zMfdoqvGJ7iSjN3av/iKzt2eWidl09Wp1cNckNlUpF\nz74mkpMUPv84hWdetFEkXC6B5weXS2HurFROHvcyUq68CJEtdrvd723evC9DiHvh92/w5s2b8+9/\n/9vfzRYK1pZtULJx49p57wV2u/ax272PeN9VKmsq0tHYlrq62oSpbaT57FxTErjm+98jwZdAnPs0\n13wJJCs3bs7Vob3txMCAnnXOTahRM8w8iNq6+yXFxw9SU3wc/dXD8KcLToWfYKRWq3hsiJkvPk5h\nxqcpjBpjwyT3T+Sp1JQbV1uSkxWeecFK8RJyxUWI7PB4PIHughCZ8nvw/+2331K0aFF/N1voXfZe\nYY97P7tde7ngu0QFTTke0Denvr4OEeqMaUFmtQkzJspqSmfalktxk+DLeHJwzZfAEfdRkpRkmuob\n0cnYDr1KRhf8Zf9eN1abispVZcT0Xul0Kv5vhIWP30th1uepDB9lkaspeST+ipcZn6RiNKl45kUr\ntjC50iKEEAVdriKR7t273/aay+Xi6NGjnD59mnfeeeeeOybgmi+BPa597Hbv54z3LGXUpWior099\nXR2Ka3I/UYlepaOEJkqq9eSjvbtc1G2gR62WINUfTGY1fxtl5cOpycyfncbAoWb5bP3sdJyH/36W\nSrnyGh4fZpHZeoUQIkTkKvhPSkq6LRXEaDTSoUMH+vbtS+fOnf3SucIoyZfEHvcB9rj2ccJ7iih1\ncRro6jLY3J9Smvyt9CP8I+Gaj5PHvfToI7My+lN4hJrhT1v5+L0Uvltqp3sfk6So+cmvh9zM/m8q\n9Rvq6d3PhEYjn6sQQoSKXAX/Gzdu9HM3BMD0qzPZlradCFU4DfR1edTUizKa0hLQFHB7d7soXkJN\nmXKSK+1vJUtpGPakhekfpRAWrqZtBymtd69+2eJk6SI7nR400r6zQb5/hBAixNxzArLdbicxMZHw\n8HBMJhnZvBeNzPVopm5EtKa8/OCGkL27XNRrqJf/p3mkYmUtjw818/WXaYSFqWnQWO5VyQ1FUVj9\ng4P1sU4eecxEo6ZS1lcIIUJRru/e+v7772nUqBE2m42yZctis9lo1KgRK1eu9Gf/CpX6prpU1FaQ\nIDGEXDjv5cI5H/UaSG3/vFSzjp6ej5hYOCeNo0fcge5OgeP1Kiyck8ZPG5w88ZRFAn8hCpg2bdpg\nNBqxWq0UK1aMLl268PvvvwM3sjXUajVWqzXDY8mSJQBMmDCB/v37Z9ru0KFD0ev1GbaLjLz9nsO6\ndesSFRWF253x+/fcuXP069ePqKgowsLCuO+++5gyZUr68ujoaEwmU4b2e/XqBcCpU6dQqVRYrVbC\nwsKIioqiU6dO6f3Ojj179tC9e3eKFi1KeHg4devW5b333kufiNblcjF+/HgqVqyIyWSiYsWKjB8/\nHpfLld7GnT6f6OhoVq1aBcCsWbNQqVSMHz8+wzpNmzZl1qxZzJ07N33/LBZL+n7dfJw+fTrb++QP\nuQr+v/32W3r06IFer2fatGnMmzePqVOnYjAY6N69O8uXL/d3P4UokPbuclGugobiUZLyk9eaP2Cg\nXScDX32RytnTUmIvuxx2hS8/TeX3Ix5GPW/lvhpyoipEQfT++++TkpLCmTNnKF68OMOGDUtfFhUV\nRUpKSoZHnz59stXuCy+8kGG7+Pj4DMv37t3LoUOHcLlc/PDDDxmWDRo0iMjISI4fP05CQgLLli2j\nUqVKGdZZtmxZhvaXLVuWYXl8fDxJSUmcPHmSv/3tb4wcOZJJkybdtd/bt2+nVatWNG7cmCNHjpCY\nmMi8efPYvn07yX+WVe/fvz8//PAD3377LSkpKSxfvpyVK1fe8WQoKxEREXzwwQdcuXLltmUDBw5M\n37/du3cDZNjn8uXL5/j97kWu0n5ef/11BgwYwJw5czK8/txzz/H4448zYcIEevTo4ZcOClFQKYrC\n3l0uWrWVPPT80rmbkaTrCjM+vTELcGRxOenKyvVEH19+lorHozD6JRtFi0opTyEKOrPZTP/+/Xn0\n0Ufz5f1mzpxJx44dqVChAjNnzqRnz57py3bs2MGUKVOw2WwAxMTEEBMTk6v3sVgsPProoyiKwpAh\nQxg1alSWpeXHjh3LoEGDeO2119Jfi4mJYcGCBQBs2LCBH374gd9//50KFSoAULt2bZYuXUq1atXY\nsGFDjiasrVatGmXKlGHy5Mm8//77udrH/JKr4P+3337LcNnmrwYNGpThf7wQhVXcSS+JCQp16stI\nan5RqVT06W8i+QsfX3ycyugXrdhsEtBm5uIFLzM+SaFoMTVDR1gxm+VzEsKfhl96xu9t1jfWZVzZ\nMVmuk5yczNy5c6lSpYrf3/9WLpeLefPm8cEHH1ChQgXatm3LpUuXKFGiBHBj4tfRo0fzzDPP0Lx5\n89tG/XOjV69ePPbYY2zfvp2uXbtmuk5aWhpbt25lwoQJd2wnNjaWJk2apAf+N1WoUIEmTZoQGxub\no+AfYNKkSTRs2JAXXngh30fzcyJX3/ZFixbl6NGjmS47evSoTPIlBLBti5NqNbSEFZGgKj9pNCoG\nDbNgtar48tNUnE4l0F0KOiePe/h4WgoVKmoZ/rQE/kKEghdeeIEiRYoQFhbGjh07MmRnXL58mfDw\n8AyPI0eOZKvd999/P8N2fw2IV6xYgcPhoGfPnrRo0YJy5cpleN9FixbRsWNH3nnnHapVq0a1atXS\n8+Rv6tu3b4b2b82bv5VerycyMpJr167dcZ2EhAR8Ph+lS2c+2SncSCe60/JSpUrdlt6UHTVq1KBf\nv35ZnnQEg1x94/fr149XXnmFGTNmkJiYCMD169eZMWMGr732Wq5ypYQIJUlJPvbtdvNAG7lxMhD0\nehXDRlpwOhS+npGK1ysnADcdOexm+kcpNGyqZ+BQMzqZHVmIkDBt2jSuX7/OsWPHUKvVHDt2LH1Z\nVFQUiYmJGR41atTIVrtjxozJsN2GDRvSl82cOZPevXun38Q6cOBAZs6cmb48PDycSZMmceDAAa5e\nvUqvXr3o27dvhsB98eLFGdqfOHFilv1xuVzEx8dnOdAcERGBWq3m/Pnzd1wnMjLyjssvXLiQfmOz\nTqe77UZmALfbjU53+5X9CRMmsHDhQn777bcs9yOQcpX289ZbbxEXF8eIESN48skn0z8YRVHo3bs3\nb775pr/7KUSB8vNmJ8Ui1VSrfs/VdEUuWaxqhj9t4cOpKSyam0b/QeZCX0lr7y4X879Okxr+QuSD\nL0p86Pc2tdq7/6ZUqVKFDz74gCeeeOKOaTH+cOHCBVavXo3ZbKZkyRuTkDqdThITE9m5cyeNGjXK\nsH6RIkUYP34877zzDidPnsx1lsi3336LTqejSZMmd1zHbDbTokULFi9eTLt27TJdp0OHDkybNo24\nuLgMqT9xcXFs376df/3rXwCUL1/+tgpDaWlpXLp06baUoZvrjxgxIsO9BsEmVyP/BoOBJUuWsH//\nfj788EPGjx/PRx99xP79+1m8eDEGg4x2isLL7VbYtsXFA20NqNUSXAVS0WIaho+ycviAmx9XOALd\nnYD6+Scn879Oo0cfEx26GCXwFyKEde3alRIlSjB9+vRsre/z+XA4HBked/P1119TtmxZjh49yr59\n+9i3bx9HjhyhVatW6aP/48aN48CBA3g8Hux2O++99x4RERFUr149x/uUlpbG4sWLeeaZZ/jHP/5x\n15OHd955h9mzZ/PWW2+lV+A5evQojz32GImJibRv357OnTvTu3dv9u/fj9fr5cCBA/Tu3ZvOnTvT\nvn174MZneerUKWbMmIHL5SI5OZmxY8fSsGFDKleunOl7v/rqq6xZsybD1Zdgku3g/9ixYzRo0CBD\nHf9atWoxatQoXn31VZ566inOnj1LgwYNOHHiRJ50VoiCYM9OF4oPmWwqSJQuq2HIcAub1jvZstEZ\n6O7kO0VRWLfawbff2Ok/yEyL1jI4I0Rh8Pe//50pU6Zgt9u5fPnybXX+//Of/6Sv+80332AymTI8\nPJ4bJZOnTZt227ZXr15l1qxZjBo1ilKlSlGyZMn0x7PPPsv8+fNxOBzY7Xb69u1LREQE5cqVY+PG\njaxcuRKLxZL+3r169crQduPGjTPsR2RkJGFhYVSoUIHPPvuMDz/8MH1UPitNmzZl48aN/Pzzz1St\nWpXw8HD69etHkyZN0qsPffPNN3Tp0oXu3btjsVh4+OGH6dKlC998802G91+1ahVz5syhZMmSVK1a\nlStXrrB48eI7DqJERkby/PPPZ3lfQiCpFEXJVjLsyJEjOXLkCJs2bcpyvbZt21KjRg0++eQTv3Qw\nGOXmJpDssNls6bVnxb3RaDRERESQkJCQPplHflAUhalvJhNTS8eD3UNnxutQODb37XYx76s0Bv/N\nQs3agavAlJ/HpqIofP+tg62bnQweZiGmVuhVngqFYzNYBOp7Mz9lNkFVXsmL41Kr1WIyhc5viwiM\nbI/8x8bGZpgw4k6GDRvG6tWr76lTQhRUx456uHzJR4tWMroabOo20NPlYSNzZxWOScB8PoVv5tn5\nZauT4aOsIRn4CyGEyLlsB//nzp27Y27TX1WsWJFz587dU6eEKKg2b3BSp76OIuFSOjEYte1goF4D\nPf/9LJXEBF+gu5NnPG6F2f9N4/BBN089Z6VyVbnxXAgRem5NR7r5mDt3bqC7FtSyHaFYrdZMpyy+\nVXx8fIZcLiEKi8uXvPx22EOrtjLqH6xuTgJWopSGLz9LweEIvRKgTqfCf6encibOw9NjrJQtJ4G/\nECI0paSkZPoYOHBgoLsW1LId/Dds2JCFCxfedb0FCxbQsGHDe+qUEAXRTxudRFfSUK6CBFvBTKNR\nMfgJM14vzPlvaM0BkJbm4/OPUki45uPp521EldQEuktCCCGCTLaD/6effppFixbx+uuvZ3ojkM/n\n44033uCbb75h9OjRfu2kEMEuLdXHru0uGfUvIExmNU+MtHDmtJflS+xks+5BUEu67uPT91PwuGHU\n81YiikrqmRBCiNtle4iye/fujBs3jtdff53p06fTvn17ypcvj0ql4vTp06xbt46LFy8yduxYHn74\n4bzssxBB55efXVitKu4PYBUZkTPFIjX83wgLn/0nheLFNTxQgE/crsZ7+fyjVMKKqBg20orJJDX8\nhQi0m+UkhQg2OcpPePvtt2nVqhVTp05l8eLFOJ03amYbjUZatGjBjBkz8nQ2OSGCkdersHWTk1Zt\nDWg0EnQVJNGVtPQfbGberDSKFlMXyJO3ixe8fP5hCqXLaRj8hAW9Xo5BIYQQd5bj5OQHH3yQBx98\nEK/Xy9WrVwEoVqwYGo3klorC6cBeNw67QuPmBXfkuDCrW1/P1Ss+5sxK5ennC9YNsnGnPHz5SSr3\n1bhxEiMnn0IIIe4m179yGo2GqKgof/ZFiAJHURQ2b3DSqJleUi0KsHadDFyN9/Hfz1J59iUb4RHB\nny9/7KibmdNTadBYT69HTajVcvwJEUxSUlL83qZWq8VoNPq9XVG4BP8vnBBBLO6kl7OnvbRsLaP+\nBZlKpaJ3PxPFS2j4bwEoAXpwv4sZn6byQBsDvftJ4C9EMFIUJU8eQtwrCf6FuAebNziJqaUlsrik\nvRV0Wq2KIX8z4/HA3JnBVwLU61U4tN/FF5+k8PWMNLo+ZKRrdxMqlQT+Qgghsk+CfyFy6do1Hwf3\nuWnVVi7BhgqzWc0TT1k4Heflu6X2QHcHuHGcrfrezuR/JjH3qzSKFFHzzEtW2nSQ404IIUTOFZw7\n24QIMls3OSldRkOlKjLqH0r+WgK0WHEND7TJ/5Qur1fht8Metm11cvRXDyVLq2nf2Uj9RnJviRBC\niHsjwb8QueB0Kmz/2UnPvmZJuwhB0ZW09HvczPyv0yhWTE1MrfwpAZqY4GP7z052bHORlqpQp76e\n0S9YKR+tkeNMCBEUXn75ZS5evMisWbMC3RWRSxL8C5ELO39xodOpqFu/4NWFF9lTr6Geq/E+5sy8\nUQK0TB6VAPX5/jfK/9thD1El1bTtYKRBYx0ms2RmCiGyZrVa0/9tt9vR6XRotTe+r1555RVeeeWV\nLLcvWbIkCxYsoE2bNnnZTRFEJPgXIod8PoUtG500f8CAViejsaGsfWcD8Ve8fPlZKs+NtVEk3H/B\neGKil21bHOz42UlKikKdejpGjbESXUlG+YUQ2ffXkqJNmzZl5MiRDB06NHAdEkFPhpWEyKEjhz0k\nJvho1lIf6K6IPKZSqeg7wEzxKA3//SwVpzN3FYBcLoX4K15OHvewe4eTD/59gTdeSWTfbhet2xn5\n56QwBgy2ULGyVgJ/IYRf+Xw+Jk2aRMWKFYmMjKRv375cunQJgF69enH58mW6du2K1Wpl4sSJAIwe\nPZqyZcsSFhZGo0aN+PnnnwO5C8LPZORfiBzavMFJg8Z6rDY5dy4MbpYA/XBqCnNnpjJ0hAW1WoWi\nKNjtCknXFZKTfCRdV0hK8pF8879JN5/7cDhutKVSgcWqolYdC08/H0aFiioJ9oUQeerzzz9n1qxZ\nrFmzhtKlSzN69GgGDhzI2rVrWbZsWaZpP02aNOGNN97AZrMxdepUHnnkEU6cOIHBIHPahAIJ/oXI\ngfNnvRz/3UPPvrZAd0XkI7PlRgnQ/7ybwruTk3G7FZKTFDyeG8s1GrCFqbCFqQkroiIsTE1USQ1h\nYTf+HVbkxjKrTYVeryUiIoKEhAS8Xm9gd0wIkWeeGX7J723WrW9kzLiyOdpmzpw5jB07lipVqgAw\ndepUihUrxrlz5yhTpkym2wwaNCj933//+9954403+OOPP7j//vtz33kRNCT4FyIHNm9wUq26llKl\npbxnYRNZXMNTz1r57Vf3jSA/TIWtyI3/mi0ygi+ECE7nzp0jOjo6/XlERARhYWFZBv9vvfUWs2bN\n4uLFi6hUKhwOB/Hx8fnUY5HXJPgXIpuSknzs3e1i6HBLoLsiAqRUGQ2lysiJnxCi4ChTpgynTp1K\nf56QkEBSUlJ64H/rwMWaNWv44IMPWLduHTExMSiKgs1mQ1GCa9ZzkXsS/AuRTdt+clK0mJr7asif\njRBCiKx9+EUJv7d5s4RnTgwcOJB3332XDh06UKpUKcaOHUubNm3Sg/8SJUpw/Pjx9Jz/5ORktFot\nkZGRuN1uJk+ejOPmjUsiJMgdi0Jkg9ut8PNPLh5oY0CtlvQOIYQQBcOIESMYNGgQ7dq1o1y5cly7\ndo25c+emL3/11Vd5/fXXCQ8PZ/LkyTz88MO0bduWqlWrUrFiRSIiIihevHgA90D4m0qR6zg5lld5\nbzabjeTk5Dxpu7DRaDR+valyxzYn3y1z8NrEMAyGwhf8y7HpP/4+Ngs7OTb9pzAcm5GRkfn2Xnlx\nXGq1Wkwmk9/bFYWLjPwLcReKovDTBifNWugLZeAvhBBCiNAhwb8Qd/HH7x4uXfTRvJXUNxZCCCFE\nwSbBvxB3sXmDkzr1dIRHyJ+LEEIIIQo2iWaEyMKVS16OHPLwQFsZ9RdCCCFEwSfBvxBZ+GmTk+hK\nGspHS3lPIYQQQhR8EvwLcQdpaT52/uKSUX8hhBBChAwZzhTiDrb/7MJqVVGzti7QXRFCCFHA2Gy2\nQHdBiEzJyL8QmfB6FbZuctKitQGNRsp7CiGEECI0SPAvRCYO7neTlqrQpJk+0F0RQgghhPAbCf6F\nyMSWjU4aNtVjMsufiBBCCCFCR8Bz/r///nvWr1/PqVOnaNasGWPHjk1fFhcXx4cffsipU6coUaIE\nI0aMoE6dOunLt27dyldffcW1a9eoXr06zz77LFFRUenL58yZw6pVq/B4PLRo0YKRI0ei093I305J\nSeHjjz9mz549mEwmevXqRY8ePfJvx0XQOhPn4dQJL48ONAe6K0IIIYQQfhXwYc2iRYvy6KOP0qlT\npwyvezweJk6cSOPGjZk/fz79+/fnrbfeIjExEYAzZ87wwQcf8NRTTzF37lyio6N555130rePjY1l\n06ZNvPvuu3z++eecPXuWuXPnpi+fPn06brebmTNnMmHCBBYvXszu3bvzZ6dFUPtpo5PqMVqiSmgC\n3RUhhBBCCL8KePDfvHlzmjZtSlhYWIbXDx48iNPppG/fvuh0Oh544AHKly/P1q1bAdi4cSP169en\nXr16GAwGHnvsMU6ePMnp06cBWLt2LT169KBkyZKEhYXRv39/1q1bB4DD4WDr1q0MGjQIs9lMdHQ0\nnTp1Ys2aNfm78yLoJCX52L/HzQNtpLynEEIIIUJPwIP/Ozl9+jTR0dGo1f/rYqVKlYiLiwNupARV\nrFgxfZnZbKZkyZLpy0+fPk2lSpUybHv9+nUSEhI4d+4ciqJQoUKF9OUVK1ZMP3EQhde2n5wUi1RT\nrUbAM+KEEEIIIfwuaCMcu92OxWLJ8JrFYuHy5cvAjdH7zJbb7fZMl9/8t91ux+FwYDab77jtreLj\n44mPj09/rlarKV68eC737M5UKhUajaSa+MPNzzEnn6fHrbBti4su3cxotUH7pxEQcmz6T26OTXFn\ncmz6jxybQhQOQRvhmEwmUlNTM7yWmpqKyWQCwGg0kpaWlmF5WlpahuV/3f7muiaTCbvdflug/9dt\nb7VkyRK++OKL9OdDhw5l9OjRudyzrOn1UlrSn25NJ8vK1s1J+HwqOnYpgcEYtBfFAkaOTf/KybEp\nsibHpn/JsSlEaAva4L98+fIsWbIEn8+Xnvpz8uRJWrVqBUCFChU4ceJE+vp2u52LFy+mp/KUL1+e\nkydPEhMTA8CJEycoUqQIERER6UH+6dOnKV++fHrbN/99qz59+tC6dev052q1moSEBD/v8Y2rD7ee\n8Ijc0Wg0hIWFkZSUhNfrvev6iqLw43fXadJMT5r9OmmZXwQqtOTY9J+cHpsia3Js+k9hODYjIiIC\n3QUhAi7gwb/X68Xr9eLz+fD5fLhcLtRqNbVq1UKv17N06VJ69OjB9u3biYuLo0WLFgC0adOGF198\nkX379hETE8O8efOIjo5OD+Dbt2/PkiVLaNCgARaLhQULFtC+fXvgxlWBFi1aMHv2bJ5//nmuXLlC\nbGwszz33XKZ9jIyMJDIyMv15fHx8nnwxKooSsl+4gXLz+Lqbk8c9nDvrZfBwi/w/yIQcm/6X3WNT\nZE2OTf+TY1OI0KZSFEUJZAfmzZvHggULMrzWrl07xowZw6lTp/joo484deoUUVFRPPnkkxnq/G/Z\nsoWvvvqKhIQE7rvvPp577rn0Ov+KojB37lx+/PFHvF4vzZs356mnnspQ5/+jjz5Kr/Pfu3fvbNf5\n/2v+vz/ZbDaSk5PzpO3CRqPREBERQUJCQrZ+xL7+MhXFB0OGW+66bmEkx6b/5PTYFFmTY9N/CsOx\n+deBPCEKq4AH/wWRBP/BLyc/YgnXfLw1IYknn7VSuUrAL4YFJTk2/acwBFj5SY5N/ykMx6YE/0IE\ncalPIfLL1s1OSpbWUKmyVLgQQgghRGiT4F8Uak6nwvafXbRsrUelUgW6O0IIIYQQeUqCf1Go7dnp\nQq2Geg3LQ3nZAAAXEElEQVSlVKAQQgghQp8E/6LQUhSFLRudNGupR6eTUX8hhBBChD4J/kWhdew3\nD1cu+2jW0hDorgghhBBC5AsJ/kWh9dNGJ3Xq6ygSLn8GQgghhCgcJOoRhdKVy16OHPbwQBsZ9RdC\nCCFE4SHBvyiUtm5yUiFaQ/loqesvhBBCiMJDgn9R6NjtCjt/cdFSRv2FEEIIUchI8C8KnZ2/ODGa\nVNSupwt0V4QQQggh8pUE/6JQ8fkUtm5y0fwBAxqNlPcUQgghROEiwb8oVI4c8nD9uo+mLWRSLyGE\nEEIUPhL8i0JlyyYn9RvqsVjl0BdCCCFE4SMRkCg0Lpz3cuyolPcUQgghROElwb8oNLZsdFK5mpZS\nZTSB7ooQQgghREBI8C8KhdQUH7t3umTUXwghhBCFmgT/olDY/rOLsDA1MTVlUi8hhBBCFF4S/IuQ\n5/UqbN3spGVrPWq1lPcUQgghROElwb8IeQf3u3HYFRo1k5QfIYQQQhRuEvyLkLdlo5OGTfSYTDLq\nL4QQQojCTYJ/EdJOx3k4dcJLi9Yy6i+EEEIIIcG/CGmb19upHqMlqoSU9xRCCCGEkOBfhKzEBA/7\ndkt5TyGEEEKImyT4FyFrw9rrFCuuoVoNKe8phBBCCAES/IsQ5XErbFibRKs2RlQqudFXCCGEEAIk\n+Bchas8uJx6PQsOmkvIjhBBCCHGTBP8i5CiKwuYNDlq1DcNgkFF/IYQQQoibJPgXIefEH17On/XS\noXORQHdFCCGEECKoSPAvQs7mDQ5q19MTWVwX6K4IIYQQQgQVCf5FSIm/4uXXgx7atDcGuitCCCGE\nEEFHgn8RUrZsdFKugoboSjLqL4QQQghxKwn+Rciwp/nYsc1Fq7ZS4UcIIYQQIjMS/IuQsf1nF2aL\nilp1ZdRfCCGEECIzEvyLkOD1KmzZ5KRlawMajZT3FEIIIYTIjAT/IiQc3O8mLVWhSXN9oLsihBBC\nCBG0JPgXIeGn9U4aN9NjMsshLYQQQghxJxIpiQIv7qSH03FeWraRG32FEEIIIbIiwb8o8DZvcBJT\nS0tkcU2gu/L/7d17dJT1ncfxz2SSyUxuNCHcakhCEAKChUSgSFCISLbqLmhBGtvSFaQFFhbwFCz1\nHHu4eFrL8ZzaiggNyEZuoSsVz1qloIhcBKVENKUgIrkIEshIxNxmJjPz7B8ss43YAskkM5Pn/fqH\nzDwzz3wnfE7OJ0+e+T0AAABhjfKPiFZ70a+yo826M5+LegEAAFwL5R8Rbf/bbvX6plVZN3PUHwAA\n4Foo/4hYbrehd99x6867YmWxsLwnAADAtVD+EbEOH/TIFmPRkFwu6gUAAHA9KP+ISH6/oX173Bp1\nZ6yioznqDwAAcD0o/4hIf/urV5cu+XX7aC7qBQAAcL0o/4hIe3e7NGyETfEJRBgAAOB60ZwQcc58\n6tXpUz7dkc9FvQAAAG4E5R8RZ+9ut7JviVaPnizvCQAAcCMo/4gol77w6+iRZt3JUX8AAIAbRvlH\nRHlnr1vdekSp/4DoUI8CAAAQcSj/iBgej6GD+z26M5+LegEAALQG5R8R48h7HlmipNxhLO8JAADQ\nGpR/RAS/39C+t9y6fbRNMTaO+gMAALQG5R8R4aPjXn3+uV+j7uSDvgAAAK1F+UdE2PeWWzm32ZSU\nRGQBAABaiyaFsHfuM59OnvCyvCcAAEAbUf4R9va95dbN/aP1zTQu6gUAANAWLJbeCjabTbGxwT8K\nHR0drcTExKDvN5J9+aVPpX/5QjPnpiox0XHdz7uyFGh8fLwMw2iv8UyDbAYP2Qwushk8ZBMwB8p/\nK3g8Hnk8nqDvNzExUXV1dUHfbyTbtcOl5OQoZfRpVl2d97qfZ7VaZbPZ1NDQIJ/P144TmgPZDB6y\nGVxkM3jMkM32OHAHRBpO+0HY8jYbOrjXrdFjYxUVxfKeAAAAbUX5R9h6/4hHXp807Ntc1AsAACAY\nKP8IS4ZhaO9bbo3Msyk2lqP+AAAAwUD5R1g6ddKr8+f8yuOiXgAAAEFD+UdY2vuWW0NyYvSNZCIK\nAAAQLDQrhJ2a8z6dOObVHXdx1B8AACCYKP8IKz6foe3bmpSZZVV6BivRAgAABBPlH2HDMAxtK2nS\n2U99KpwaF+pxAAAAOh3KP8LGrtfdOnrEoxmz49U11RrqcQAAADodyj/CwrvvuPXGDpemPhKvtHRO\n9wEAAGgPlH+E3PFjzdpW0qTJDzk0cFBMqMcBAADotCj/CKmqSq9eXNeg8ffYNeJ2VvcBAABoT5R/\nhIyzxqd1zzcod5hNd3+H4g8AANDeKP8Iifo6v9aualB6plXf/Z5DFosl1CMBAAB0epR/dDi329C6\n1Q1yxFn0w2nxslop/gAAAB2B8o8O5fMZ2ri+QY0NhqbPildsLMUfAACgo1D+0WEMw9DLf2hSVYVP\nP54Tr8RE4gcAANCRaF/oMG/+2a0jhz16ZHa8UrtxES8AAICORvlHh3jvoFs7X3Np6vR4pWdwES8A\nAIBQoPyj3Z34W7Ne2tKkSYUO3TKYi3gBAACECuUf7epMlVcvrm3QuH+J1bdHsZY/AABAKFH+0W4+\nd/q09vkGDb3NpoJ77aEeBwAAwPQo/2gXDfV+FT3XoLTeVk0q5CJeAAAA4YDyj6DzeAy9sLpBdrtF\nUx/hIl4AAADhgvKPoPL5DG36rwbV1xt6ZDYX8QIAAAgnrLmIoKmr82vj+kadP+fTnAUJSkzid0sA\nAIBwQvlHUFRVeFW8tkFJXaK04LFEfSOZ4g8AABBuKP9os0MH3Hr5v5s0bIRN9z/oUEwMp/oAAACE\nI8o/Wq252dDLf2hS6WGPvjvFwTr+AAAAYY7yj1apvehX8doG1df5NefRBPXOIEoAAADhjsaGG3by\nRLM2rm/UTWlWzZidqIREzu8HAACIBJR/XDfDMPTWLrd2vOrSmHGx+s6/2lnDHwAAIIJQ/nFdXE2G\ntm5s1MkTzfrhtDh9K8cW6pEAAABwgyj/uKbz1T4VFzVIkuYtSlSPntYQTwQAAIDWoPzjn/rwfY+2\nbmxU/wEx+t7UONntnOYDAAAQqSj/+Fo+n6HX/8elvbvd+s6/2ZV/d6wsFoo/AABAJKP84yr1dX5t\nXN+oc5/59OM58eqXHRPqkQAAABAElH+0UFXp1YtrG5SQGKUFjyUqOYVlPAEAADoLyj8kSTXnfXrj\nzy6VHm7W8JE2PTDFoZgYTvMBAADoTCj/Jvf3pT/r5mjN/M943dyf03wAAAA6I8q/SV0479MbO1x6\n/y/NyuoXrVnzEtS3H3EAAADozGh7JvP3pb9vv2jNmp+gvjcTAwAAADOg9ZkEpR8AAAC0v07uQvXl\nc/rf/0uzbu4frdnzE5RF6QcAADAlWmAndaHap107XDp6hNIPAACAy2iDncxXS/9/LEhQn778NwMA\nAIDy3yk0Nvr1tzKvPnzfo+PHvJR+AAAAfC3aYYSqr/Prrx82q+xosz7+yCtHnEWDvxWjOY8mKDOL\n/1YAAABcjZYYQS594VfZ0WZ9eNSj8k98Sky0aPDQGOWPj1efvtGyWrkiLwAAAP4xyn+Y+9zpU9nR\ny0f4Kyt8Sk6x6NahNt07waH0TKuioij8AAAAuD6U/zB0odqnD/+v8J8941O3HlH61pAYPTDFoZt6\nW2WxUPgBAABw4yj/YWTX619q3546na/2q9c3o3TrUJse+vc49egZReEHAABAm1H+w4jHY+i2ETbd\nOjRG3bpbQz0OAAAAOhnTl//6+no999xzKi0tlcPh0AMPPKCJEyeGZJb7JnZRXV1dSF4bAAAAnZ/p\ny/+aNWvU3Nys9evX68KFC3riiSeUlpam2267LdSjAQAAAEEVFeoBQsnlcunAgQOaOnWq4uLilJmZ\nqYKCAu3atSvUowEAAABBZ+ryf/bsWRmGoYyMjMB9ffr0UVVVVQinAgAAANqHqU/7cblciouLa3Ff\nfHy8mpqaWtzndDrldDoDt6OiotStW7egz2OxWGS18kHfYLjyfeT7GRxkM3jIZnCRzeAhm4A5mLr8\n2+32q4p+Y2OjHA5Hi/u2bdumoqKiwO2HH35Yc+fObZeZbDZbu+zXrJKSkkI9QqdBNoOLbAYP2Qwu\nsgl0bqYu/zfddJMkqaqqSunp6ZKk8vLywNdXTJo0SWPGjAncjoqKUm1tbdDniY+PV0NDQ9D3a0ZW\nq1VJSUn68ssv5fP5Qj1OxCObwUM2g4tsBo8ZspmcnBzqEYCQM3X5t9vtysvL04YNG/Too4+qpqZG\nO3fu1Pz581s8LjU1VampqYHbTqezXX4wGobRaX/ghorP5+N7GgRkM/jIZnCQzeAjm0DnZuryL0kz\nZ87UypUr9fDDD8vhcGjSpEks8wkAAIBOyfTlPyEhQYsXLw71GAAAAEC7M/VSnwAAAICZUP4BAAAA\nk6D8AwAAACZB+QcAAABMgvIPAAAAmATlHwAAADAJi2EYRqiHAILN6XRq27ZtmjRpUosLtAGhRjYR\nrsgmYA4c+Uen5HQ6VVRUJKfTGepRgBbIJsIV2QTMgfIPAAAAmATlHwAAADAJ65IlS5aEegigPTgc\nDg0bNkxxcXGhHgVogWwiXJFNoPPjA78AAACASXDaDwAAAGASlH8AAADAJKJDPQBwLa+++qp2796t\niooK3X777Vq0aFFg25EjR1RcXKzq6mr16tVLs2fP1oABAwLbX375Zf3pT39SXV2dBg8erLlz5yo5\nOTmwfePGjdqxY4e8Xq/y8vI0a9YsxcTEdOj7Q2Rqbm7W6tWr9cEHH6iurk6pqamaMmWKxowZI0mq\nrKzUs88+q4qKCvXo0UM/+clPNGTIkMDzDxw4oOLiYl28eFEDBgzQvHnz1L1798B2sonWaks2L168\nqFWrVunUqVOBr9PS0lrsn2wCkY0j/wh7KSkpmjJligoKClrcf+7cOa1YsULTpk3Tli1bdN9992n5\n8uWqr6+XJO3Zs0evvfaali9frg0bNqhLly56+umnA8/fuXOn3n77bT399NP6/e9/rzNnzmjTpk0d\n+t4QuXw+n1JSUvTkk0+qpKREc+bM0fPPP68TJ07I6/Vq+fLlGjFihLZs2aLCwkL96le/0hdffCFJ\n+vTTT/Xb3/5Ws2fP1qZNm5SZmakVK1YE9k020RZtyabFYlFubq4ef/zxr9032QQiH+UfYW/UqFEa\nOXKkkpKSWtxfWlqq7Oxs5eTkyGq1qqCgQA6HQ4cOHZIkHTp0SOPHj1evXr1ks9lUWFiosrIyVVdX\nS5LeeOMNTZw4UT179lRSUpIKCwv15ptvdvj7Q2Sy2+36wQ9+oJ49e8piseiWW27RwIEDdfz4cZWV\nlcntdmvy5MmKiYnRHXfcofT0dB04cEDS5V9Mc3NzlZOTo9jYWH3/+99XeXm5qqqqJJFNtE1bspmc\nnKx7771X/fv3/9p9k00g8lH+EbEMw9DXLVZVUVHxDx//99urqqqUlZUV2J6VlaVLly6ptrY26LOi\n83O5XDp16pQyMjJUVVWlzMxMRUX9/4/YrKwsVVZWSrp82kWfPn0C2+Li4tSzZ8/AdrKJYLqRbF4L\n2QQiH+UfEWvo0KE6fvy4jhw5Iq/Xqx07dqimpkZut1uSNGzYMO3cuVNnz56V2+3Wli1bZLFYAttd\nLpfi4+MD+7vydVNTU8e/GUQ0v9+vZ555Rv369VNOTo6amppaZEu6nK8r2fpq9q61nWyitW40m9dC\nNoHIxwd+EbHS0tK0cOFCFRcXy+l0avjw4RoyZIi6du0qSRo3bpycTqeWLFkit9ut+++/X3a7Xamp\nqZIu/2m8oaEhsL/GxkZJly9yA1wvwzC0atUqXbx4UUuXLpXFYpHD4WiRLUlqaGgIZMtutwfydkVj\nY2OL7WQTbdWabF4L2QQiH0f+EdFGjhyp3/3ud9q8ebPmzZunM2fOKDs7W9LlD64VFhaqqKhIL774\nokaMGCHDMJSRkSFJSk9PV3l5eWBfp0+fVpcuXVqsBgT8M4ZhaPXq1SovL9eSJUsCBSg9PV2VlZXy\n+/2Bx5aXlweyl5GRodOnTwe2NTU1qbq6mmwiaFqbzWshm0Dko/wj7Pl8Pnk8Hvn9fvn9fnk8Hnm9\nXknSxx9/LJ/Pp/r6ehUVFal79+7KycmRJNXX1+uzzz6TYRiqrq7WypUrNXHiRCUkJEi6/JeBV155\nRdXV1aqrq1NJSYnGjRsXsveJyLNmzRp99NFHWrp0qeLi4gL333rrrbLZbPrjH/+o5uZm7d+/X5WV\nlcrLy5MkjR07VqWlpTp69Kg8Ho82b96szMxMpaenSyKbaLvWZlOSPB6PPB6PpMvLhno8nsBnpsgm\nEPksxtd9YhIII5s3b1ZJSUmL++666y4tWLBAjz/+uD755BNFRUVp+PDhmjFjRmBVoHPnzmnZsmWq\nqalRQkKCCgoKVFhYGPigm2EY2rRpk15//XX5fD6NGjVKs2fPZr1qXJcLFy5oxowZiomJkdVqDdw/\nefJkTZkyRRUVFVq5cqUqKirUvXt3zZw5s8U6//v371dxcbFqa2uVnZ2t+fPnB9b5J5toi7Zmc8KE\nCVfts6ioSD169CCbQCdA+QcAAABMgtN+AAAAAJOg/AMAAAAmQfkHAAAATILyDwAAAJgE5R8AAAAw\nCco/AAAAYBKUfwAAAMAkKP8AAACASVD+AUSM/Px8DRw4UB6P56ptDz74oHr37q36+voQTAYAQGSg\n/AOIGKtXr1Z5eblWrFjR4v4dO3bopZde0rPPPquEhIQQTQcAQPizGIZhhHoIALheS5cu1VNPPaVj\nx44pKytLLpdLgwcP1uDBg7V9+/Z2f32XyyW73d7urwMAQHvgyD+AiPLzn/9cGRkZmjNnjiTpl7/8\npc6fP6+VK1cGHlNZWamHHnpIXbt2lcPhUH5+vj744IMW+1m3bp1GjRqllJQUpaSkaNy4cSotLW3x\nmMWLFys1NVXvvPOORowYodjYWK1du7b93yQAAO0kOtQDAMCNsNlsWrNmjcaOHasnn3xSK1as0FNP\nPaW0tDRJUk1NjfLy8pSamqpVq1YpISFBv/nNb5Sfn69Tp04pJSVF0uVfEKZPnx7468GGDRs0evRo\nHTt2TH369Am8XmNjo6ZOnapFixYpOztb3bp1C8n7BgAgGDjtB0BEmj59utavX6/c3Fy99957slqt\nkqSf/exnWrdunU6ePBko+k1NTerbt69mzJihZcuWXbUvn88nv9+v/v37a9q0afrFL34h6fKR/1//\n+tfavn27Jk6c2HFvDgCAdsJpPwAi0uLFiyVJP/3pTwPFX5J27typ8ePHKykpSV6vV16vVzExMRo9\nerQOHz4ceFxZWZkmTJig7t27Kzo6WjabTRUVFTp58mSL17FYLLrnnns65k0BANDOOO0HQESy2Wwt\n/r3C6XSqpKREJSUlVz1n0KBBkqTa2loVFBQoLS1NzzzzjNLT02W32/WjH/1ILperxXOSk5Oveg0A\nACIV5R9Ap5KSkqLc3Fw98cQTV21zOBySpH379qm6ulp79uxRdnZ2YHttbe1Vz7FYLO03LAAAHYzy\nD6BTufvuu/XKK69o0KBBgbL/VU1NTZJa/tVg9+7dqq6u7pAZAQAIFco/gE7lscce09atW5Wfn6+5\nc+eqd+/eunDhgg4ePKi+fftqzpw5ysvLk8Ph0KxZs7Rw4UJVVlZq2bJl6tWrV6jHBwCgXfGBXwCd\nSo8ePfTuu+9q4MCBWrhwoQoKCrRw4UKdPXtWw4cPlySlpaVp69atqqqq0oQJE7Rq1Sq98MILSk9P\nD/H0AAC0L5b6BAAAAEyCI/8AAACASVD+AQAAAJOg/AMAAAAmQfkHAAAATILyDwAAAJgE5R8AAAAw\nCco/AAAAYBKUfwAAAMAkKP8AAACASVD+AQAAAJOg/AMAAAAmQfkHAAAATOJ/AWZ2B8nMsN4ZAAAA\nAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11be5f320>"
+       "<matplotlib.figure.Figure at 0x11b8afd30>"
       ]
      },
      "metadata": {},
@@ -5289,10 +5652,10 @@
     {
      "data": {
       "text/plain": [
-       "<ggplot: (296865663)>"
+       "<ggplot: (298268399)>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5306,21 +5669,30 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "The blue line above represents the compounding net flux of inmates from each year. In other words, what the total population would be if the population started at 0 in the first year, (not the true total population because the dataset does not contain the initial population count)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "From these two plots, we can gather that there is evidence for a widescale change in North Carolina's criminal justice system that resulted in changes the population flux and recidivism rates. "
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### How often did each method select the best model?\n",
     "\n",
@@ -5329,14 +5701,137 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
+   "execution_count": 85,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>actual_precision</th>\n",
+       "      <th>estimated_precision</th>\n",
+       "      <th>model</th>\n",
+       "      <th>test_year</th>\n",
+       "      <th>type</th>\n",
+       "      <th>diff_in_prec</th>\n",
+       "      <th>abs_diff_in_prec</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.916</td>\n",
+       "      <td>extra1</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>0.026</td>\n",
+       "      <td>0.026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.86</td>\n",
+       "      <td>0.890</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>temporal</td>\n",
+       "      <td>0.030</td>\n",
+       "      <td>0.030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.93</td>\n",
+       "      <td>0.922</td>\n",
+       "      <td>rf2</td>\n",
+       "      <td>1986</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>-0.008</td>\n",
+       "      <td>0.008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.93</td>\n",
+       "      <td>0.880</td>\n",
+       "      <td>rf1</td>\n",
+       "      <td>1986</td>\n",
+       "      <td>temporal</td>\n",
+       "      <td>-0.050</td>\n",
+       "      <td>0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.94</td>\n",
+       "      <td>0.922</td>\n",
+       "      <td>rf2</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>-0.018</td>\n",
+       "      <td>0.018</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    actual_precision  estimated_precision   model  test_year      type  \\\n",
+       "2               0.89                0.916  extra1       1985  standard   \n",
+       "0               0.86                0.890     rf1       1985  temporal   \n",
+       "11              0.93                0.922     rf2       1986  standard   \n",
+       "8               0.93                0.880     rf1       1986  temporal   \n",
+       "17              0.94                0.922     rf2       1987  standard   \n",
+       "\n",
+       "    diff_in_prec  abs_diff_in_prec  \n",
+       "2          0.026             0.026  \n",
+       "0          0.030             0.030  \n",
+       "11        -0.008             0.008  \n",
+       "8         -0.050             0.050  \n",
+       "17        -0.018             0.018  "
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c_temp_vs_std.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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Xl6fs7GwFBASoRo0acrlcWrx4sdq2batNmzZpz549Z50363a75Xa7vcsej8fv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\npkwZVa5cWV26dLmo85g5c6a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FAACoDLfddpvWr1+va6+9VqNHj7a7HPx/Zxxa09PTlZ6eLumPO62DBg1SrVq1zllhAAAA\ndvj444//cp+T815ReTya0/rwww97uw4AAIBK9VOW9+eZ1vhHByX2HeD1ceFhaC0uLtb//u//6o03\n3tDu3btLPd/M4XBo27ZtXikQAAAA8Ci0jhw5UlOmTFF6erpat27NIxsAAABwTnkUWl999VU98sgj\nevDBB71dDwAAAFCKR5+IdezYMTVv3tzbtQAAAABl8ii03nTTTXr33Xe9XQsAAAC8KDEx8YyehnCm\nRo0apaysLK+NdzY8mh7QtGlTjR07Vvv371e7du0UFRVVap9u3bpVuDgAAABfNWvWLM2YMUPr16+3\nu5QqwaPQ2rdvX0nSzp079dprr5Xa7nA4VFRUVLHKAAAAYIvCwkL5+3sUE88Zj6YHbN++/bSvn3/+\n2dt1AgAAVFmTJ09WnTp1FB4ervPOO0+PPvqoBg8erI0bN6pGjRqqUaOG8vPz9fnnn6t58+aKiopS\nYmKi7rjjDh0/ftw9jsPh0PPPP6+LLrpIkZGR6tOnjwoKCtzbn3rqKSUnJ6tmzZqaMmVKiRrOZOzp\n06froosucv8W/dtvv9VVV12l8PBwZWRkKDs7+xx/p8rnUWhNTU39yxcAAACkLVu26KGHHtLy5cuV\nl5entWvXqnv37poxY4auuuoqHTlyREeOHFFYWJicTqcmT54sl8ulDRs26JNPPtHUqVNLjPfGG29o\n7dq12rp1qz799FO98sorkqRly5ZpwoQJevfdd7Vr1y5t3bpVLpfLfdyZjP36669r1apVOnjwoE6c\nOKHMzEx17dpVhw4d0t13362XX3753H/DyuFRaN21a9dfvgAAACD5+/vLsixt2rRJR48eVVJSki6+\n+OIy97388svVvHlz+fv7KzU1Vbfeeqs++eSTEvs88MADio2NVc2aNZWRkaEvv/xSkjRv3jxlZWWp\nUaNGCg4O1qRJk1RcXHxWY48aNUo1a9ZUSEiIPv30U+Xn52vUqFEKCAhQ+/bt1aFDBy9/d86cR5MV\n0tLS5HA4TrsPc1oBAACkevXqafbs2frPf/6jAQMGqFWrVqV+dX/Sjz/+qGHDhunzzz/X77//rsLC\nQjVs2LDEPomJie6vQ0ND9euvv0qS9u7dW2LfqKgoRUREnNXYp/62fO/evUpOTpafn1+J7Tk5OR58\nFyrOozutixYt0ptvvlniNXPmTPXs2VMpKSmaO3eut+sEAACosnr27KlPPvlEv/76q+rVq6dBgwaV\neQPw9ttv19/+9jf9+OOPys3N1cSJE2VZ1hmdo1atWiV+252Tk6Pc3NyzGvvUmmrVqqU9e/aUuFtr\n52/TPQqtmZmZpV5ZWVmaN2+eunbtWupWMwAAQHW1ZcsWLV++XMeOHVNQUJBq1Kghp9OphIQE7dmz\np8QfQ+Xl5SkiIkLh4eH68ccfNWPGjDM+zw033KDZs2fr66+/1rFjx/TAAw+UuEt6tmM3a9ZMISEh\nevzxx3XixAktX75c77///tl/A7zEo9B6Oh07dtT8+fO9PSwAAECVdPz4cY0ZM0bx8fGKjY3V+vXr\nNWPGDLVp00Z///vflZSUpKioKOXn52vy5Ml6/fXXFR4eroEDB6pHjx5nfJ4OHTpo9OjRysjIUEpK\niurVq6e4uDj39rMdOyAgQG+//bbeeOMNRUdH66mnnnI/9tQODutM7zmfofHjx2vGjBnau3evN4c9\na6f+tZwnwsPDlZeXV2p9zvAhFRrXNGOHT7C7BK96JrH0B11I1aeff5s1X9nZ2T4/p7y8fvoKp9Op\n6Ohoeukj6OfZOTVknWs/ZfXy+pg1/tFBiX0HeH1cePiHWEOHDi21rqCgQN9//73WrFmj4cOHV7gw\nAAAA4CSPQuu7775bal1wcLBq166t6dOn65ZbbqlwYQAAAMBJHoXW7du3e7sOAAAAoFxe/0MsAAAA\nwNs8Dq1fffWVevTooaSkJAUFBSkpKUk9e/bU119/7c36AAAAAM+mB6xevVrt2rVTYmKibrzxRiUk\nJGj//v1atGiRmjVrpmXLlqlly5berhUAAMBrAuqe5/Ux/Svx6QfVjUehddSoUbrmmmu0ePFi+fv/\nd4gnnnhCGRkZGjVqlNasWeO1IgEAALwt9eF/210CzoJH0wO++uorDR06tERglf54Ft3QoUP15Zdf\neqU4AAAAQPIwtIaFhenAgQNlbtu/f7/CwsIqVBQAAABwKo+mB3Tq1EkjR45U7dq11bZtW/f65cuX\na/To0ercubPXCgQAADgXem36yetjdoisoQF1Er0+LjwMrVOmTNHmzZvVoUMHRUREqGbNmjpw4IBy\nc3N11VVXafLkyd6u86wFBgYqKCjI4+P9/f0VHh5ean1ORYrCOVdWz6Tq1c+wsDB5+dOZjVNeP32F\nw+GQRC99Bf0EvMOj0BodHa1PP/1Uixcv1po1a5Sdna2YmBi1bNlSGRkZ8vOz//GvBQUFKigo8Pj4\n6vB52L6ovJ5Vp37m5+fz+eZVnNPpVGBgIL30EfTz7FTkhhN8m0ehdcWKFdq1a5cGDBhQairArFmz\nlJqaqtatW3ulQAAAAMCjW6Jjx47V/v37y9x28OBBjR07tkJFAQAAAKfyKLRu3rxZV155ZZnbGjVq\npM2bN1eoKAAAAJgpKytLo0aNqvTzehRaHQ6HDh8+XOa27Oxsn5+zAwAAcDbS0tL0/vvv211GleZR\naG3SpImmTZtW6q8gLcvS9OnT1aRJE68UBwAAgMpTWFhodwnl8ii0PvLII1q3bp0uu+wyTZkyRXPn\nztXkyZPVsGFDrVu3ThMmTPB2nQAAAFXSjTfeqF27dqlr166qUaOGxowZo61bt+q6665TXFyc6tWr\np+nTp7v3HzdunLp3766bb75ZEREROv/88/XZZ59pzpw5Sk1NVVxcnJ566qkS+3fr1k19+vRReHi4\nLrnkEn3yySfu7b/++qu6d++uuLg41a1bV//+979VXFwsSfr444+VmJiop59+WsnJybr++uvdNScl\nJSkyMlKtWrXSd999V0nfrfJ5FFqbNWumFStWKCIiQiNHjlSfPn00atQoRUZGasWKFWratKm36wQA\nAKiS5s2bp5SUFC1atEhHjhzRmDFj9I9//EOdO3fWvn37tGTJEk2aNEnLli1zH7N48WJ16dJF2dnZ\n6tKli3r06KF169bphx9+0HvvvaeRI0fql19+ce//zjvvqEOHDsrOztb999+vzMxMZWdnS/ojgEZG\nRmrXrl1atmyZXnzxRc2cOdN9rMvl0vbt27Vt2zYtWrRIktS+fXtt2bJFBw4cUOPGjXXjjTdW0ner\nfB4/ULVFixZau3at8vLytHv3buXm5mr16tVq0aKFN+sDAADwKYsXL1ZSUpJuv/12BQQE6MILL9Sg\nQYM0b9489z7NmjVT586d5XQ63Xdqx40bp5CQEDVp0kRpaWn65ptv3Ps3bNhQffv2lb+/v/r376+6\ndevqvffe0+7du/XJJ59oypQpCg0N1d/+9jcNHz5cc+bMcR9rWZYmTpyo4OBghYSESJIGDBigiIgI\nBQUF6aGHHtLmzZv122+/Vd43qQwePaf1VCEhIe4LBAAAwOnt2LFDX331laKiotzrioqK1KpVK/dy\nYuJ/Pwo2NDS0zHVHjhxxL6ekpJQ4R2pqqvbs2aM9e/YoMjJS0dHR7m1paWnas2ePezkuLs59jpO1\njBkzRgsWLNDBgwfdHxrlcrkUGxvr8XVXlP0fXQUAAODjTn6cr/RHwGzevLlycnLcr7y8PC1ZssTj\n8Xft2lVqOTk5WcnJyTp8+LBycv77weU7duxQcnJymbVJ0ty5c/Xmm29q2bJlOnz4sHbu3ClJtn8M\nMaEVAADgHEtISNC2bdskSddff7127NihmTNn6vjx4yosLNR3332njRs3ejz+N998o7lz56qwsFAv\nv/yytm3bpo4dO6p27dpq1aqVhg8frqNHj2rbtm2aMmWK+vbtW+5YeXl5CgoKUmxsrI4ePWrMh0YR\nWgEAAM6x0aNH6/HHH1dUVJQee+wxLVu2TO+8847q1Kmj+Ph43XrrrcrNzfV4/M6dO2vJkiWKjo7W\nxIkTtWjRIsXExEj64w/BfvvtN9WuXVtt2rRRv379dMstt5Q7Vr9+/XTeeecpOTlZ9evX11VXXeVx\nXd5U4TmtAAAAOL3MzExlZmaWWPf222+Xue+4ceNKLF900UWlfjX/9ddfl1gODAzUK6+8UuZ4tWrV\ncj8V4M+uueYa/frrryXW1ahRo1Rt/fr1c389a9asMsc617jTCgAAAOMRWgEAAGA8pgcAAABUYX+e\nTuCruNMKAAAA4xFaAQAAYDymBwAAgGqpQ2QNr49ZP4xPCT1XCK0AAKBaGlAn8a93gjGYHgAAAADj\nEVoBAABgPEIrAAAAjEdoBQAAgPEIrQAAADAeoRUAAADGI7QCAADAeIRWAAAAGI/QCgAAAOPxiVgA\nYKBem36yuwSveiYxyu4SAFRx3GkFAACA8QitAAAAMB6hFQAAAMYjtAIAAMB4hFYAAAAYj9AKAAAA\n4xFaAQAAYDxCKwAAAIxHaAUAAIDxCK0AAAAwHqEVAAAAxiO0AgAAwHiEVgAAABiP0AoAAADjEVoB\nAABgPH+7C/grTz/9tFatWiV///+WOm3aNMXHx9tYFQAAACqT8aFVkjIzM9W/f3+7ywAAAIBNmB4A\nAAAA41WJO60ffPCBPvjgA8XFxalTp05q166d3SUBAACgEhkfWjt16qSbb75ZYWFh2rx5sx577DGF\nhYWpefPmJfZzuVxyuVzuZT8/vwrNe3U4HHI6nR4fD3uU17Pq1M/qcJ2+3k9fvLbq/N48eX2+fp1S\n9egn7GN8aK1Xr57768suu0wZGRlau3ZtqdC6cOFCvfDCC+7lrKwsDRkypELnDgwMLLXutwqNiHMt\nOjq63G3VpZ8RERF2l1ApyuqnbzlgdwFedbbvTV/EexOoGOND6585HA5ZllVqfffu3ZWenu5e9vPz\nU3Z2tsfnCQsLU35+vsfHwx7l9bw69TM3N1dFRUV2l3FO+Xo/ffFOVXV+bzqdTkVERPDePEOn+wcO\nqjfjQ+uaNWvUqFEjBQcH64cfftB7772nW2+9tdR+cXFxiouLcy+7XK4K/XCwLMvnf7j4ovJ6Vp36\nWVRU5PPXWp366St4b/LeBCrK+NC6ePFiTZs2TcXFxYqLi1OfPn109dVX210WAAAAKpHxoXXSpEl2\nlwAAAACb8ZxWAAAAGI/QCgAAAOMRWgEAAGA8QisAAACMR2gFAACA8QitAAAAMB6hFQAAAMYjtAIA\nAMB4hFYAAAAYj9AKAAAA4xFaAQAAYDxCKwAAAIxHaAUAAIDxCK0AAAAwHqEVAAAAxiO0AgAAwHj+\ndhcAwHt6bfrJ7hK86pnEKLtLAAAYgjutAAAAMB6hFQAAAMYjtAIAAMB4hFYAAAAYj9AKAAAA4xFa\nAQAAYDxCKwAAAIxHaAUAAIDxCK0AAAAwHqEVAAAAxiO0AgAAwHiEVgAAABiP0AoAAADjEVoBAABg\nPEIrAAAAjEdoBQAAgPEIrQAAADAeoRUAAADGI7QCAADAeIRWAAAAGM/f7gIAAPiznOFD7C7Bq6Jn\nzbe7BKDK404rAAAAjEdoBQAAgPEIrQAAADAeoRUAAADGI7QCAADAeIRWAAAAGI/QCgAAAOMRWgEA\nAGA8QisAAACMR2gFAACA8Xz2Y1wDAwMVFBTk8fH+/v4KDw8vtT6nIkXhnCurZxL9rKrOtp++wuFw\nSPrN7jK8ivemFBYWJsuy7C7jnPL19ybs5bOhtaCgQAUFBR4fHx4erry8PC9WhMpQXs/oZ9VUXfvp\ndDrtLsHrqmsvT5Wfn6+ioiK7yzinvNHPitxwgm9jegAAAACMR2gFAACA8QitAAAAMB6hFQAAAMYj\ntAIAAMB4hFYAAAAYj9AKAAAA4xFaAQAAYDxCKwAAAIxHaAUAAIDxCK0AAAAwHqEVAAAAxiO0AgAA\nwHiEVgAAABiP0AoAAADjEVoBAABgPEIrAAAAjEdoBQAAgPEIrQAAADAeoRUAAADGI7QCAADAeIRW\nAAAAGI/QCgAAAOMRWgEAAGA8QisAAACMR2gFAACA8QitAAAAMB6hFQAAAMYjtAIAAMB4/nYXAADe\nkDN8iN0leNfwCXZXAC/qteknu0vwqmcSo+wuAdUQd1oBAABgPEIrAAAAjEdoBQAAgPEIrQAAADAe\noRUAAADGI7QCAADAeIRWAAClaqJ8AAAPsUlEQVQAGI/QCgAAAOMRWgEAAGA8QisAAACMR2gFAACA\n8QitAAAAMB6hFQAAAMYjtAIAAMB4hFYAAAAYz9/uAs7EkSNHNG3aNH355ZcKCQlR165dlZmZaXdZ\nAAAAqCRVIrQ+99xzOnHihF566SUdOHBADz74oGrXrq0rrrjC7tIAAABQCYyfHnDs2DGtXbtWffv2\nVWhoqNLS0tS+fXstW7bM7tIAAABQSYwPrXv27JFlWUpNTXWvq1u3rnbt2mVjVQAAAKhMxk8POHbs\nmEJDQ0usCwsL09GjR0usc7lccrlc7mU/Pz/Fx8d7fF6HwyGn0+nx8bBHeT2jn1UT/fQd9NK30E/Y\nwfjQGhwcXCqg/v777woJCSmxbuHChXrhhRfcy1lZWRoyZEiFzh0YGFhqXfSs+RUa0zS+dTWnRz99\ny5/7SS+rLt6bvqWsfgLeYHxoTU5OliTt2rVLKSkpkqTt27e7vz6pe/fuSk9Pdy/7+fkpOzvb4/OG\nhYUpPz/f4+OrAqfTqYiICOXm5qqoqMjucs4p+ulbfL2f9NK30M+zEx0d7aVq4GuMD63BwcFq0aKF\nXn75Zd177706ePCgPvzwQ919990l9ouLi1NcXJx72eVyVeiHg2VZPv/D5aSioiKfv1b66VuqSz/p\npW+hn0DFGB9aJem2227TM888o6ysLIWEhKh79+487goAAKAaqRKhtUaNGho1apTdZQAAAMAmxj/y\nCgAAACC0AgAAwHiEVgAAABiP0AoAAADjEVoBAABgPEIrAAAAjEdoBQAAgPEIrQAAADAeoRUAAADG\nI7QCAADAeIRWAAAAGI/QCgAAAOM5LMuy7C4C9nC5XFq4cKG6d++uuLg4u8tBBdFP30EvfQv9BLyD\nO63VmMvl0gsvvCCXy2V3KfAC+uk76KVvoZ+AdxBaAQAAYDxCKwAAAIznHDdu3Di7i4B9QkJCdOWV\nVyo0NNTuUuAF9NN30EvfQj+BiuMPsQAAAGA8pgcAAADAeIRWAAAAGM/f7gJgj8WLF2vlypXasWOH\nmjVrphEjRthdEjx04sQJzZgxQ998843y8vIUFxennj17Kj093e7S4IFnnnlGn3/+uY4eParw8HC1\nb99ePXv2tLssVFBubq5uv/12JSUlafLkyXaXA1RJhNZqKiYmRj179tTXX3+tvLw8u8tBBRQVFSkm\nJkYTJkxQQkKCvv/+e40fP14JCQm66KKL7C4PZ6lz584aNGiQgoKCdPDgQY0bN061atVSy5Yt7S4N\nFfDSSy+pTp06KiwstLsUoMpiekA11bx5czVt2lQRERF2l4IKCg4O1k033aTExEQ5HA5dfPHFql+/\nvr7//nu7S4MHUlJSFBQU5F52OBzau3evjRWhojZt2qS9e/eqbdu2dpcCVGmEVsDHHDt2TD/99JNS\nU1PtLgUemj17tnr06KGBAwfq2LFjat26td0lwUMnTpzQc889p8GDB8vhcNhdDlClMT0A8CHFxcV6\n+umndf755+vyyy+3uxx4qH///urXr59++uknrV+/XmFhYXaXBA8tXLhQDRs2VN26dfXzzz/bXQ5Q\npXGnFfARlmVp+vTpOnTokEaMGMFdnSrO4XDo/PPPV0BAgObNm2d3OfDA3r17tWLFCvXu3dvuUgCf\nwJ1WwAdYlqUZM2Zo+/btevTRRxUSEmJ3SfCS4uJi7du3z+4y4IHvv/9e2dnZGjx4sCSpoKBABQUF\n6tevn2bMmMGnYwFnidBaTRUVFamoqEjFxcUqLi5WQUGB/Pz85O/PfxJV0XPPPactW7ZowoQJ/I+w\nCjty5Ig2btyoJk2aKDg4WD/88IOWLl2qG264we7S4IGWLVuqUaNG7uXVq1fro48+0kMPPcQ/LAEP\nkFCqqddee03z5893L69du1Zt2rTRPffcY2NV8MSBAwe0ZMkSBQQE6Oabb3av/+c//8nzPaug5cuX\n6/nnn1dxcbFiYmLUpUsXZWRk2F0WPBAUFFTiSRBhYWFyOp2Kjo62sSqg6nJYlmXZXQQAAABwOvwh\nFgAAAIxHaAUAAIDxCK0AAAAwHqEVAAAAxiO0AgAAwHiEVgAAABiP0AoAAADjEVoBAABgPEIrAAAA\njEdoBaqpjz/+WA6HQ59//rndpWjZsmVq0KCBgoKCFBUVZXc55brnnnuUlpZ21selpaVpyJAh3i8I\nAKoRf7sLAIABAwbosssu0/Tp0xUSEmJ3OQAAAxFagSrCsiwVFBQoKCjI7lK86siRI9qzZ4/Gjx+v\nVq1a2V0OAMBQTA8ADJWVlaVLL71US5YsUcOGDRUUFKT58+dryJAhuvDCCxUaGqq0tDQNHjxYhw8f\nLnHsyV9HT5s2TampqYqMjFSXLl108ODB057z/fffV2hoqB5++GFJUk5OjgYNGqTk5GQFBwerTp06\n6tWr11ldx3PPPacLL7xQQUFBSktL04QJE1RcXCxJmjVrlsLDwyVJAwcOlMPhUFZW1l+OOWvWLPfU\nhvbt2ys0NFQXXnihli9fruLiYo0dO1YJCQlKSEjQ6NGj3ec7adWqVWrevLlCQkIUFxenm2++WYcO\nHSqxz969e9W5c2eFhoYqOTlZjz/+eJm17N69W3369FFcXJxCQkJ09dVX64svvjht/Zs3b1bHjh0V\nGxvrrr288QEAf+BOK2CwvXv3aujQoRo7dqxSUlIUHh6u9evX61//+pfi4+P1yy+/6F//+pe6dOmi\njz76qMSx77zzjrZu3app06bJ5XLp3nvv1V133aX58+eXea4333xTvXv31oQJEzR8+HBJ0rBhw7R0\n6VJNmjRJaWlp2rdvn5YuXXrG9U+dOlVDhw7VXXfdpeuvv17r1q3TuHHjlJOTo8mTJysjI0PLli1T\nu3btNHbsWGVkZCg+Pv6Mx+/Xr58GDx6s++67T5MmTVK3bt3Uv39/5ebmas6cOdqwYYMefvhhNWjQ\nQL1795YkffHFF2rXrp2uueYaLViwQPv379eoUaO0efNmrVu3Tk6nU5KUmZmp3bt369lnn1VUVJQm\nTZqkX375Rf7+//2xmZ2drZYtW6pGjRqaOnWqIiMjNXXqVLVp00Zbt25VzZo1y6y7U6dOSkhI0MyZ\nMxUZGamffvpJu3fvPuPrBoBqyQJgpP79+1uSrPXr15e7z4kTJ6w1a9ZYkqwtW7a416emplq1a9e2\njh075l738MMPWwEBAVZRUZFlWZb10UcfWZKsjRs3WnPmzLECAgKsZ599tsT4l1xyiTVs2DCP6i8s\nLLTi4uKsXr16lVg/evRoKzAw0HK5XJZlWVZ2drYlyXrppZfOeOyXXnrJkmRNnz7dve67776zJFlN\nmzYtse8VV1xhdenSxb3ctWtXKyUlxSooKHCv++CDDyxJ1jvvvGNZlmUtXbrUkmStWLHCvU9OTo4V\nHh5upaamutc99NBDVmRkpLV//373umPHjlkpKSnWiBEj3OtSU1OtO++807Isyzp48GCJcwEAzgzT\nAwCDxcbGqkmTJiXWvfzyy7r88stVo0YNBQQEqGXLlpKkH3/8scR+6enpJea/XnzxxTpx4oQOHDhQ\nYr/nn39eAwcO1MyZMzV48OAS2xo1aqRZs2Zp8uTJ2rRp01nV/sMPP8jlcqlHjx4l1t9www0qKCjQ\nZ599dlbjlaVdu3bury+44AJJ0j/+8Y8S+1xwwQX65Zdf3MurV69WZmamAgIC3Ovat2+vqKgorVmz\nRpK0YcMGRUZGqk2bNu59IiMj1bZt2xJjf/jhh2rdurViYmJUWFiowsJCOZ1Opaena+PGjWXWHBsb\nq9TUVI0ePVqzZ8/mDisAnCFCK2CwhISEEsuLFi1Sv3791LhxY73++utav369Fi1aJEk6duxYiX3/\n/OiowMDAMvdbuHChUlJSlJGRUer8U6dOVd++fTVlyhQ1aNBAKSkpevbZZ8+o9uzs7DKv4eTyn+eQ\neuLUazx5fWVd96nXnJ2dXaqmk3WdrGnfvn1lTlP483Eul0tvvfWWAgICSrxefvnlEkH5VA6HQx9+\n+KHq16+vO++8U3Xq1NGVV16pVatWneFVA0D1RGgFDOZwOEosL1iwQH//+9/13HPPqWPHjmrSpImi\no6MrdI45c+bI399fHTp0UG5uboltkZGRevrpp7Vv3z59++23at++ve644w6tXr36L8eNiYmRpFJ3\ndvfv319ie2WLiYkpVZP0R10na0pKSirzj9ZO1n7qWNdee602btxY6nXyHxNlueCCC7RgwQJlZ2fr\n448/VlBQkDp16qQjR45U8OoAwHcRWoEq5OjRo+47iie9+uqrFRozISFBK1as0KFDh3TdddcpPz+/\nzP0aNGigp556SpL0/fff/+W4F154oeLj47VgwYIS619//XUFBgaqcePGFarbUy1bttRbb72lwsJC\n97ply5YpJyfHPdWicePGOnz4sFauXOne5/Dhw1q+fHmJsdq2bav/+7//U/369XXllVeWeDVo0OAv\nawkICFB6erpGjRql3Nxc7d2710tXCQC+h6cHAFVIu3btdOedd+rRRx9Vs2bNtGTJEq1YsaLC4yYn\nJ2vFihW6+uqr1blzZ7333nsKDg5WixYt1LVrV1166aVyOp2aM2eOAgMDz+h5qk6nUw8++KCGDh2q\nmjVrqmPHjlq/fr0ee+wx3XPPPYqNja1w3Z4YM2aMmjdvruuvv1533XWX++kBjRs3VseOHSVJ1157\nrRo1aqSbbrpJjz32mKKiojRx4kRFRESUGGvYsGF69dVXlZ6errvvvlspKSk6ePCgNmzYoFq1aune\ne+8tdf5vv/1W9913n2644QbVq1dPhw8f1sSJE5WWlqZ69epVyvcAAKoiQitQhdx22236+eefNXXq\nVD3xxBPq0KGD5s6dq6ZNm1Z47LS0NK1cuVJXX321unXrprfeekstWrTQnDlztH37dvn5+alBgwZ6\n9913Vb9+/TMa86677lJAQICefPJJTZ8+XUlJSRo3bpweeOCBCtfrqSuuuEIffvihRo8ere7duyss\nLEydO3fWlClT3I+7cjgcevvttzV48GDddtttio6Odgfct956yz1WbGys1q9fr7Fjx2rkyJH67bff\nVLNmTTVt2lRdu3Yt8/yJiYlKTEzUxIkTtWfPHkVGRqpVq1Z65ZVX3OcHAJTmsCzLsrsIAAAA4HSY\n0woAAADjMT0AgEeKiop0ul/UnPrJUWejuLi41MeunsrpdJZ6qgIAwPdxpxWAR+rVq1fq+aSnvjw1\nfvz40447e/ZsL14FAKCqYE4rAI989913On78eLnbr7zySo/G3bt372kf/VS3bl3bnjwAALAPoRUA\nAADGY3oAAAAAjEdoBQAAgPEIrQAAADAeoRUAAADGI7QCAADAeIRWAAAAGI/QCgAAAOP9P1CtuvYL\nttmDAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11bfe5198>"
+       "<matplotlib.figure.Figure at 0x11ba8e8d0>"
       ]
      },
      "metadata": {},
@@ -5345,16 +5840,16 @@
     {
      "data": {
       "text/plain": [
-       "<ggplot: (297365915)>"
+       "<ggplot: (297828984)>"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "c_for_ranking = c_temporal_results_df.pivot(index='validation_year', columns='model', values='val_precision')\n",
+    "c_for_ranking = c_temporal_cv_results_df.pivot(index='validation_year', columns='model', values='val_precision')\n",
     "c_rank = c_for_ranking.rank(axis=1, ascending=False, method='min')\n",
     "\n",
     "ranks = []\n",
@@ -5362,8 +5857,7 @@
     "    year = row[\"test_year\"]\n",
     "    model = row[\"model\"]\n",
     "    \n",
-    "    if year != test_end_year:\n",
-    "        ranks.append(c_rank.at[year, model])\n",
+    "    ranks.append(c_rank.at[year, model])\n",
     "\n",
     "c_temp_vs_std[\"ranks_of_models\"] = ranks\n",
     "\n",
@@ -5374,19 +5868,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
+   "execution_count": 90,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "type\n",
-       "standard    1.815789\n",
-       "temporal    2.263158\n",
+       "standard    1.714286\n",
+       "temporal    1.714286\n",
        "Name: ranks_of_models, dtype: float64"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5398,14 +5896,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
-    "We can see that, in this respect, standard CV did often select a higher ranked model than temporal. Arguably more important is model regret."
+    "They are strangely the same."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Model Regret\n",
     "\n",
@@ -5414,18 +5918,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
+   "execution_count": 91,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
-    "c_temp_vs_std = c_temp_vs_std.merge(for_ranking.max(axis=1).to_frame().reset_index(), \\\n",
+    "c_temp_vs_std = c_temp_vs_std.merge(c_for_ranking.max(axis=1).to_frame().reset_index(), \\\n",
     "                                    left_on='test_year', right_on='validation_year')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
+   "execution_count": 92,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "c_temp_vs_std.rename({0:'best_precision'}, inplace=True, axis='columns')"
@@ -5433,30 +5945,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
+   "execution_count": 93,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [],
    "source": [
-    "c_temp_vs_std['regret'] = c_temp_vs_std['best_precision'] - c_temp_vs_std['test_precisions']"
+    "c_temp_vs_std['regret'] = c_temp_vs_std['best_precision'] - c_temp_vs_std['actual_precision']"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Here is the regret by year, followed by the regret density."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
+   "execution_count": 94,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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i2yCiujAMw5b/yB5VBdzHHnsM1157Lfbaa69RG8pmzZo1qn0Y5XvguijgRqVA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\nujrgmkrVdJNZodShN2ci6nP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llRIRDEIluYJLRAQw4NpKV43RQQGw6lHDLuqFGzPsHfJQ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\nRU5ErkJggg==\n",
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Rq2GAS1kyHITwtU24ndC9qe2ZxW0ahi3hGmPxoFthDS4REbUWBriUJYvN4Op6ansGuE3D\nLDDkISNVosAAl4iIWgcDXMoqepGZ2wMIwQC3iRQa05vBRWZERNRqGOASAEBaFmCaE47pBQChKBCa\nDjvGVmHNwpCpfreFcJEZERG1Gga4BCA9xQyAUkQGF2AnhWaTWWRWCBeZERFRq2GASwBSLcIAQBRR\ngwukOilw2EPzMO1xShS4yIyIiFoMA1wCkA5wPRqEw1nU9kL3clxvEzG4yIyIiKYQBrgEALCLHNOb\nwRKF5jLRIjNLAjaDXCIiahEMcAlA8VPMMhjgNhdTYpwa3NQNFuNbIiJqEQxwCUDxPXAzWIPbXAwp\nx+misGMbIiKiVsAAlwAU3wM3Q+heZnCbyESLzAB2UiAiotbBAJcAlF6ioOg6F5k1EWOCGlyAAS4R\nEbUOBrgEIJXBLbYHLpCqwbWZwW0apsSYXRScSL0RGJxmRkRELYIBLgEoZ5GZFzBikMlkFY+KKkFK\nOe6gByFEalwv41siImoRDHAJ0rbTi8xKy+ACgDRi1TosqpA4ABtjj+oFUgvNuMiMiIhaBQNcSnVD\nkBKilD64mr7jvtTQMrW1Y9XgZm5jDS4REbUKBrgEGQ4DQGklCtkAlxncRmfaRQS4HNdLREQthAEu\nwQ6HAEWB0LSi7yMcDsDtZga3CRjpuHWsRWaZ27jIjIiIWgUDXIIMBSF8fohxAqBChMZWYc0gk5l1\njfPycpEZERG1Ega4lFpgVkL9bYaie5nBbQKmlHAJQBm3Bpd9cImIqHUwwKWSp5hlpMb1MoPb6Awp\nxy1PANIlCgxwiYioRTDApZJ74GZw2ENzGG9MbwYXmRERUSthgEslTzHLEBzX2xQMOX4HBYA1uERE\n1FoY4NIkMriswW0GppTwTLB+0CM4qpeIiFoHA1xKTzHzlXw/obMGtxmYUsI9zhQzgIMeiIiotTDA\npfIXmeleBrhNoNhFZgxwiYioVTDAneKkZQGmCeFvK/m+QtNYotAEuMiMiIimGga4U5wdDgFAmSUK\nXshYFJKBUUMzCywyk8lkzuXMIjO+lvWRSCSQHPWa1JqUEgm+/kTUIhjgTnEyHeCKMgJcRdcB2wZM\ns9KHRRVkFFhkFvz+FTDf/kf2skcAEoDF+KYubvvbG3j8tTfqegwvGHFcvzVU12MgIqoUR70PoBG4\nXC643e6q7NvhcMBfxpSwWoklk4hoOto6u0q+b2L6DIQA6IqAo8q/Y2aMsNfrZZaxRInhKNo0T/Y8\nlHELw8HtsD/YBP+8DwEAuqw4sDUMh9cLv0Ot5+E2nUqcm0NuDYMuF77o85U8MrtSVm/fgiFbNsT7\nVaO/bzYLvm/SVMYAF4BlWbAsqyr79vv9CIUaNytibvkAwucr6xhluq1UaGALHK7qfEDIUFUVLpcL\nkUik7l/lNptoIgFYVvY1toPbAQDW4AAS6esSSRsAsDUUgsoAtySVODejThdCug9/GxrGbGft35a3\nJ228FjMhAQwHg1DrFGRnNPr7ZrOYCu+b1UpOUfNjicIUV24PXACAywWoKjspNDjDzu2ikHm9Etu2\nZq/zpNuIcaFZfcTcHjjjFlZHjLo8/otGHM70KRJlP2QiagEMcKe4cnvgAqmvv9gLt/GNXmSWmT6X\n3LYte50TgEBq6hnVlmmZSDgc2OPva/FizIJdhw8ZawwL+3hcAIAoP+QQUQtggDvF2eFg+RlcAELz\nQsbYKqxRSSlhSAn3iG+cM63dkkM7MrhCCLhFqqUY1VYkGgMA7LNuDUIQeDte26+SBxNJvBtP4iA9\nFeBGeA4QUQtggDvFyXB4cgEuM7gNLQHAxo4SBACwsyUK23IWnniEgMHsXc1F0hn1mZoHHwluw+pY\nddYDjGWNEUfAoWCW0wGPYIkCEbUGBrhTnB0OQWGA27IyNbU5JQrRCODRgEQcMhzOXs9xvfURMSwo\ndhLenWZh93fexEtGvKb9aFcbFhakyxO8isISBSJqCQxwp7hJLTJDZlwvSxQalZHOxo1eZKYGegEA\n9vCOOlwPp5nVRdiy4DFNqIE+zH9lNeJS4nUzUZPH3hBPYlPCxgKPEwCgC8ESBSJqCQxwpzBp26lF\nZpPoN6lozOA2MjMdq4xeZKZ0dEDx+mAPD2Wvz0wzo9qKJhLQLBNqTwCuLR9gV6eCNUZtyhTWGBZm\nO1VMT7eG0xWBqG3X5LGJiKqJAe4UJqMRQMrJ1+DGGOA2qh0lCjuuk9EohOaF2jUNMifA3ZHxpdqJ\nJpPQE3GoMwOAENgrsh1rzTisKmfTpZRYY8SxTzp7C6QDXGbxiagFMMCdwjL1l5MNcG1mcBuWISVc\nAlBG1eAKXYejaxrsoR0BLheZ1UfUltATCQiXC8q0bnx003ooANYZ8ao+7rvxJLYlbeyVrr8FUiUK\nXGRGRK2AAe4UZodDgKpCaFrZ+2ANbmMzpcwpTwDSGVxdh9rZlVOi4BKi6llDyheRgG6nWoOpPb1Q\nNm3EHm4nVlc5wF1jWPiwy4EOdcc/A16FNbhE1BoY4E5hMpTqgSsmMZaTXRQam2kXCHBjUSiaDrVr\nWk6AywxufUSFAh2pulcl0At700Ys0Fz4uxmvWj2sLSVeNOLZxWUZLFEgolbBAHcKm8wUswyh6UDc\ngkxUN9tE5TFkbgcFIFOi4E2VKOR1Uaj1EVJMVaGlXyO1J4Dkpo34iFOFRxFYW6Us7ltWAhFbYs/R\nAS67KBBRi2CAO4XZk2wRBqRKFABApqcxUWMxR08xs21Iw0iVKHRNgwwGsx9OuMisPqKqA950mYAa\n6IWMRqCEw9jL48SaKgW4q404dnE74FVy/wnwKgIxngNE1AIY4E5hk+2BC6RKFACwDrdBGaNqcKUR\nS3XO0HSoXV0AAHv7dgAc9FAvMacLXjXVpkuZNh1wOJDcvBH7eFx4w0ogmKxsmUJcSrxsxLPDHUbS\nFQURKWHzPCCiJscAdwqb7BQzABAeDRCCrcIalCllzpjeTL200L1Q2zsARcm2CmOAWx9Rlxu60wEA\nEKoKdUYPkps3Yq5TRYci8FKFs7ivmQkkpMTubmfebboQkABLVYio6THAncIqksFVFAiPxgxugxq9\nyCzzOgldS7127R3ZhWZcZFZ7iUQCptsDr8udvU7pCcDetBGKEFiguSo+9GGNYWE3jxNuJX9xqTd9\nXYTDHoioyTHAncIqscgMYC/cRjZ6kZmMRgGXC8KRyt4pHZ2wh1ILzTKLzCSD3JqJpL/58Hp2BLhq\noBfJzZsAAPt4nPhnPImtiWRFHs+0JV4149inQHkCkOqiAICdFIio6THAncLsdJuwyRKazgxug8pb\nZBaLZhcGAukAN1uiAEgA7IdRO9GYAQDwjuhFrfakAlxp2+hzqJipKhVbbPaqGYcKgV3cjoK3u4SA\nA+CwByJqegxwpyhpmoBlQfjbJr2v1LhedlFoRKMHPWSmmGUonSMD3NR27KRQOxEjHeCOeE3UQC8Q\nt2Bv2wohBPapYJnCasPCnh4nnOP0vtYVTjMjoubHAHeKsiOpMb2VKVHgNLNGZRRYZKZoIwLc9s7s\nuN5MKQMXmtVOxLTgtkw4HDsyqqK9A/BoSG7eCABY4HFiQ8LGpkmWKURsG6+ZCezjyV9cNpJXEYjw\nHCCiJscAd4qS4RAAQFSoBpfTzBpT/iKzaH4Gd3tuBpcBbu1E4nFoppFznRACak8AdroOd4ZDxSyH\nijWxyWVx1xpxeBWBD7sKlydk6IIZXCJqfgxwpygZCkFoWnax0WQwg9u4TImcGlw7PcUsQ+noAgwD\nMhaDSwACqYVpVBvRRBJ6PD9wzUw0y9hHc2K1EZ/UAsDVRhx7e5xQJhjN7VU4zYyImh8D3CmqElPM\nMoSmMYPboEwp87ooiBElCqKjEwBgD2+DEAJuwQxuLUVtG1oykXd9qpPCjgB3b48LW5M21pdZpjCc\ntPGWlSg43GE0TVEQ4zlARE2OAe4UVYkeuBlC93LQQwNKSIkEkFuiMKqLgtA0wO2GPTwMpLflIrPa\niUhAT+YHrWpPH+yBLdkxyp2qgnlOFatj5XVTeMmw0KUqmONUJ9zWK5jBJaLmxwB3iqpUD1wAUFiD\n25AymVi3MroGd0dLKiFETi9cTjOrragQ0GX+UAUlEABsG/aWLdnrFmguvGhYZY3RXW3EscDjhJig\nPAHIdFHgoAciam4McKcoOxyE8E2+RRiQzuAaMUj+o9hQMuNWc0oUYhEIzZuzndLRldMqjAFu7USF\nAr3A9YruhWhrzylT2MvjRMiW+IeVX9IwnoFEEv+KJ4sqTwDYRYGIWgMD3ClKhsMV6aAApAY9QEpI\ng71wG0mm1CCzyExKmddFAQCUjhHjehXBRWY1FHU4stPDRlN7AjkBrk9RMN/lKHnowxojjoBDQV8R\n5QkAuygQUWtggDtF2eEQlIrV4KYCJpYpNBYjU6KQyeBaFpBM5tTgAoDS2ZUzzYwZ3NqJOVzQHYUD\nTzXQm9NJAQD20Vx4yYgjUcJrtMawis7eAhz0QEStgQHuFFXRRWYaA9xGZEoJJwA1HeBmFgIqeRnc\nTsihESUKDG5qJuZyweso3JdW6ckPcHd3OxGXEq+ZxZUpbIgnsSlhY8EEwx1G0hWBOACLH3SIqIkx\nwJ2CpG2nFpn5KxTgOp2A0wUZYy/cRmJKOWqBWer1GdkmDEi1CrO3D0HaNjxCZDO/VF22bSPm9sDr\nKhx8qoFeyOGhnNIfjyLwMY+z6NG9awwLs50qpo+RJS7Em/5AxCwuETUzBrhTkIxGACkrlsEFOM2s\nERkyd4GZHY0CigK43TnbKR2dgG1DhoJcZFZDhmnAVlR4R70eGeqMHkAIJNMTzTL28TjxihmfMMMq\npcQaIz7haN7RMjXBDHCJqJkxwJ2CZDgMABUOcDnNrNGkxvTuuCzTU8xGt4pSOjoAAPbwULoGt5ZH\nOXWFo6nMrFfTCt4uXC4o07pzFpoBwL+5nVAAvDrBYrN340lsS9rYq4T6WyD1oUgAiPKDDhE1MQa4\nU5AdCgKqmmryXyHshdt4TClzhzwU6KAAAMLhhPD7YQ8NpbsoMLCphUjMADB2gAsAaoE6XKcQ2MPj\nmrBMYY1h4cMuBzrU0t7mFSGgC4EI2/4RURNjgDsFZRaYFdP0vVhCY4DbaIxCY3pHdVDIyPTC5SKz\n2olaFtREAi7X2BlWJdALe1SJApAqU/i7mRhzIIMtJV5MD3coBzspEFGzY4A7BVVyilmG0HXYHNfb\nUPIWmcWieQvMMpSOTtjDQ/CwBrdmIpYFzTSgKGO/Das9ASQ3bYQc9Zp8xOWApgisHaNM4S0rgYgt\nsedkAlyeB0TUxBjgTkF2BVuEZbAGt/GYEqNKFCIFSxSAdKuw4W1wCw56qJVoIgE9bo67jRrohYxG\nIEOh3OuFwF4eJ1aPEeCuNuLYxe2Ad5zgeTxeIRBhBpeImhgD3Cmokj1wM9hFofGYtoQnZ5FZ4Rpc\nABCdnbCHhrKDHkZnDKnyIkkbemL8hWLKtOmAw4Hk5g15ty3wuPCmlUAwmVumEJcSLxvxkoY7jMYS\nBSJqdgxwp6BKTjHLYIDbeIwCi8wUbawa3HSJgiJgAyhtGCyVI2pLaMnkuNsIVYU6oyevVRgAzHWq\n6FAEXhyVxX3NTCAhJXZ3l1eeALBEgYiaHwPcKagqGVxN56CHBjO6i4IdG69EoQsyEoYrmcjel6or\nKgR0OXGnAiXQC3tUJwUg1e1ggZbfTWGNYWF3jzOn/rpUOksUiAr6zW9+g5/+9Kf1PgwqAgPcKag6\ni8y8kNEYv9puIMW2CQPSwx4AuILbU/dlcFN1UQjomPh5VnsCBTO4QKqbwjvxJAYTqUywaUu8Msny\nBADwskSBqCAGuM2jIQLccDiM66+/Hl/84hexZMkSPPzww2Nuu27dOpx77rn4whe+gG9961t45513\nCm5300034eijj8b7779frcNuWnYoCOFvq+g+ha4DyQRgFTdClKrPkKnRrhnjdVEQPh+gOuDcvj17\nX6quqOpA4YKRXGpPL5KbN0EWaAnW51DRoypYky5TeMWMwyEEdnE7JnVsuqKwRIGImlpDBLi33347\n4vE4fv7zn+Oqq67Cr3/9a6xZsyZvu2AwiO9///v4/Oc/jxUrVuBTn/oUrrnmGsTjuTVor776Kj74\n4INaHX5TkaYJWFbFSxQytZ2SrcIaxshFZjKRAExzzD64QlGgdHTAMbQ1dV8GN1UXczihFzGEQQ30\nAnEL9ratebeJUWUKawwLe3r/CJ9ZAAAgAElEQVSccE6yx7UuxJg9dommqiVLluCee+7B3/72Nwgh\nIITAtGnTIITAW2+9lbPt0NAQNE3LZnuXLFmCj33sY/j973+Pj33sY/B4PFiwYAGee+65vMe5++67\nsfvuu8Pj8aCvrw+XXXYZkhPU61O+uge4hmHg2Wefxcknnwxd1zFnzhwcfvjhWLlyZd62q1atQiAQ\nwCGHHAKn04lFixZBSomXX345u008Hscdd9yBc845p5a/RtOwI6kxvdXogwuArcIaiCElXOlAJ/PB\nY6wSBSBVpiCGh+ASDHBrIep0weuYONMq2jsAj5Y3sjdjH48TGxM2/mEl8JqZwD5l9r4dyasIxCSQ\n5HlAlPWf//mf+NznPoedd94Zq1atwqpVq/D888+jr68Py5cvz9n2vvvuAwCceOKJ2es2bdqEr33t\na7joootw//33w+12Y+HChdiyZUt2mx//+Mc488wzsXDhQvz2t7/Fd77zHfzkJz/BZZddVptfsoXU\nPcDdsGEDpJSYPXt29rq5c+di/fr1eduuX78ec+fOzV4WQmDOnDk52z7wwAPYe++90d/fX90Db1Iy\nnOqnKSoc4MLtBhQFNjspNISklEgA2UlmmQ4X4wW4YsSwB47rrb6Y2wPdNXEwKoSA2hMouNAMAKY7\nVPQ7VPxyexReReDDrsmVJwCpLgoAEON5QJQ1b948TJ8+HZqm4eMf/zg+/vGPY968eTjttNPwi1/8\nIifLunz5cnz+859HR0dH9rpt27bhrrvuwqmnnoqjjz4av//97yGlxI033ggACIVCuPLKK3HxxRdj\n2bJlOOyww/CNb3wDy5Ytw09+8hNs3Zr/LQ6Nre4BrmEY0Ef9o+v1ehGLxfK2jcVi8Hq9Y267ceNG\nPPnkk/jSl75UvQNucjIUgtA0CMfkszwjCSHSnRQY4DaCTAZ2R4CbyqyPVYMLAEonx/XWimVZiDud\n8LrcRW2vBnrHXGgGAAs0JwaTNvb2OKFUYAS3nt4HF5qVL5FIwGaZx5RwxhlnYNOmTXj88ccBAK+8\n8gpefPFFnHHGGTnbtbe345BDDsm5fOihh+Kvf/0rAOAvf/kLwuEwjjvuOCQSiex/hx56KGKxGNat\nW1e7X6oFTP6j/iR5PJ68YDYajULTtLxtNU1DdFSGMBKJZLf97//+b5xyyikF7zvS4OAgBgcHs5cV\nRcH06dPL/RXGJYSAqqpV2Xc54tEIhM9flWMSXi+EEavKvjP7bKTnspHF03GJ7lChqiqSpgnh0eBw\n7vhgM/rcdHR2If7KS/AoAlaDnbeNrJxz0zBSE8z8ulbU/Zy9fTD+7+kxt93P68HvwgY+7i1ufxPx\npyegGXU6DxrtfbMc1/79LRzuAPbbZX7djoHvm7UxZ84cHHbYYbjrrrtw5JFHYvny5Zg7dy4+85nP\n5GxXKM6YOXMmXnvtNQDIxiV77713wcd57733Knzkra2sAHf9+vUIBAJwOvOzgIlEAhs3biy6RKCv\nry+7z8x93nnnnYL37+/vxx/+8IfsZSkl3n33XRxxxBEAgLVr1+Jf//oXbr/99uw2S5cuxcknn4yF\nCxdmr3vggQdw5513Zi8vWbIE5557blHHWw6Xa3IteyppWzKBZGcnOjs7K77vSFsbPFJWZd8ZbW2V\n7f7QqiKGBXwwjJmdnfA5VAQFEPP7816bkedmZFY/osND8DqdUNyeqr6OraiUc3NbKAggib6eANra\nJ75f7CPzseGh/0WHzwdR4H23E8CdXV3wlDmatxDP5iEI3YtOfzG9Hiqvkd43yzHo9WMour0h/o74\nvll9Z511Fk488URs2LAB//M//4NvfOMbEKO+TRkYGMi73wcffIBAIAAA6OrqAgA8+OCDmDVrVt62\nI0s0aWJlBbhz587FqlWrsN9+++XdtnbtWuy3335Fr/jzeDz45Cc/iXvvvRcXXHABBgYG8Mc//hHn\nn39+3rYHHHAA7r77bjz55JM48MAD8dhjjwEA9txzTwDAPffck7P9qaeeiksvvRTz5s3LuX7x4sX4\n9Kc/nb2sKAqGhoaKOt5Seb1eRCKNs/AquuUD2B6tKr9v0uVGZHAQqMK+VVVFW1sbgsEgV5MWYYuV\n6ixiBLcjLgRiA1sg3Z6c1330uZlwuiAtCw7LwlAUGBqa/FfdU0E55+aWga0QLh+suFXU36Lt9QO2\njcE3Xoejb6cxt8sv7CqfLgS2BEMYStS+9V+jvW+WyrIsmG4Ptg0NVO3flmJMhffNWn+AcLlcMAwj\n7/pFixahs7MTJ554IrZt24YlS5bkbbN9+3b8+c9/zpYpbN++HX/605/w9a9/HUAqxtF1He+//z6O\nPfbYqv4eU0FZAe54zfxN04TbXVxdWcbZZ5+NW265BUuWLIGmaVi8eDEWLFgAADj++ONx5ZVXYtdd\nd0VbWxsuvfRS3H777bj11lvR39+Pyy+/PJtJLnSit7W15R1Pd3c3uru7s5cHBwer9scvpWyoN5Zk\nKAjh9VfnmDQddiRc1d83mUw21PPZqKKJZOqP27aRBJAMhyF0Pee5yzs3072RnZaJmMvB57lEpZyb\nIdOERzqz95uQxwPR1o74xvchegKTOcyi6UIglKjP31ujvW+WajgYBCAQQpGvb5XxfbNydtllFyxf\nvhwrVqzAhz/8YXR3d2POnDlwOp049dRTsWzZMixcuLBgBrarqwtnnHEGrr76anR0dOC6666DlBLf\n/OY3AQAdHR347ne/i4svvhjvv/8+Dj74YKiqin/+8594+OGH8cADD+StWaKxFR3gvv766/j73/+e\nvfzUU0/lDVEwDAMrVqzAzjvvXNJB+Hw+LF26tOBt999/f87l3XbbDbfccktR+33kkUdKOo6pQIbD\nULpnVGXfQtMhtw9XZd9UmlKmmGUIjwdC0+C2TJg230SrKRJPQINZ0n3UngCSY3RSqAZdEeyiUKbt\nkQjg8CGk1H2ZC1XYGWecgeeffx7nnXcetm7dilNPPRV33303AODYY4/FsmXLcPrppxe8byAQwPXX\nX4+LLroIb7/9NnbddVf84Q9/wMyZM7PbXHjhhejr68OPf/xj3HzzzXA6nZg3bx6OOuqopi/bqbWi\n//r+3//7f7j66qsBpBYAjBWQdnR0ZF9sajx2OARnhYc8ZCi6jsQYvTqptsyCU8wmrqUUHV1wGTFs\nl/WvG2xl0WQCOkr76j/VSaGGAa4QiLALQFlCMQPw+xAuUC9Nza2trQ0rVqwoeNvvf/97TJs2DYsW\nLRrz/kceeSSOPPLIcR/jS1/6ErtBVUDRAe43v/lNLFmyBFJK7LzzznjwwQex11575WzjcrnQ09OT\nV1hNjUOGQhD+6gS4Qtez/Vapvgwp4R7xZyijUSjpBQzjUTo64YpG2Ae3yqK2hI5ESfdRenphrX2p\nSkeUT1cE24SVKZiugQ+7PHU+EqqFN954A2+88QZuvvlmfP3rXy+5TJOqo+gAt729He3t7QBSXQ4C\ngQDT5U1G2jZkJAylShlcoXthc5JZQ0iN6R1ZohApKoOrdHTCFQ5xklmVRSWg26XVRKqBXsjhIUgj\nBuEZvxViJXgVgY0JZnDLEU4kIGwbUU1DMplkm64Wd/bZZ+O5557DZz/7WVxyySX1PhxKK6tAKDN1\n7PHHH8cLL7yA9957D5dffjn6+/vxzDPP4EMf+hB6e3sreqA0eTIaAaSEqFaAy0EPDWPkmF4gXaJQ\nxOIEpbMTrnCQAW6VRYSAt8TnWJ3ZAwiB5OZNcMwpbZ1DOXTBDG65QrbEtNB2DLZ3IhKNoq1K35pR\nY3jqqacm3Ialm7VXVoA7MDCAY445Bs899xxmzZqF9957D1/96lfR39+P5cuXw+v14tZbb630sdIk\nyXAYAKoX4Oo6YJqQySQEMxZ1Zcr8DK6iF5fBdb7+JgwGNlUVU1RMt0srURBOF5Rp3Uhu3libAJcl\nCmULCYGeWASD7Z0IRSIMcFtELBYbt4tUudgZoTrK6gr+zW9+EwMDA1i3bh3+8Y9/5Lzghx56KJ54\n4omKHSBVjh0KAqoKMcGkt3JlMoSsw60/UwLu9CIzaduQsVhxGdyOTriCw8zgVllUdcBbxlAGNdBb\ns04KuiIQ5XlQlrCiYgZsKMkkggXGzlNzSiQS2ZZrlfyPqqOsDO6jjz6KO++8E7vsskveizNr1qy8\n9mHUGGQ4BOHzV20RoEhnCGUsAjBjUVeGlPBnAlzTSJWmaMUEuF1wmSYD3CqLOV0lLzIDUgvNkv/8\nRxWOKJ9XCERsCSklFw6XKOx0oS1hw2vEEIrXflAGEZWZwU0kEvB6C3/dOTQ0xMVnDUqGw1B8vqrt\nP7PwhRnc+hu5yCzzehSTwRXt7XDHLSQhEGeQWzVRlxveMlpIqT2pDG41viYdTVcU2Eh9G0ClCbs8\n8Dsd8FkGgvF4vQ+HaEoqK8Ddf//9sXz58oK3/epXv8InP/nJSR0UVYedzuBWi1BVwKOlFrNRXY0c\n9JB5PYoKcFUVnvQHVGZxqyOZTMLwaPC6S08EqIEAZDQCGQpW4chyedPfALBMoTTJZBIRjwa/2w1f\n3EIoyU4URPVQVoB7zTXX4He/+x0+9alP4dZbb4UQAr/5zW9w3HHH4ZFHHskOhKDGIqsc4AKpYQ/M\n4NafIUdlcJ1OCGdxAZUnXaPNhWbVEU13GvGW0StTmTYdcDhqMvBBT58/HPZQmqgRg62qaNc0+Owk\nwvwzoibT09NTVGeIYi1duhRLliyp2P6KVVaAe8ABB+DJJ5+EEAIXXnghpJT4/ve/j02bNuGJJ57A\n3nvvXenjpAqww6Gq9cDNYKuwxmBKZAc9yGgkWx9dDI/Pm90HVV4kZgAAvGUs9hSqCnVGT00WmrkE\noALspFCiYCj1jYnf64VfSoQUdpShyrn77rvx8Y9/vN6H0RTKHpR9wAEH4Omnn0YsFsPQ0BA6OjrY\n6qLByXAIYs68qj6G0HXYzODWnSnlji4KsVhJnTO09IcglihUR8QwAKcTul5eNxMl0At786YKH1U+\nIQQ7KZQhFItBcTvg1f3wKQLvqmX/M0vUVBKJBByOxjnfS87gGoaB9vZ2/Pa3vwUAaJqG3t5eBrdN\nQIZCVV1kBqQ6KbAGt/6MnEVmpWVwHZ2dcCbiHNdbJRHTgsuy4CqyZGQ0tSdQs1ZhmU4KVLygaUKP\nxaCqKvwOB8Iujm2l8txwww2YNWsW/H4/dt55Z3zve9/DV7/6Vbzwwgvw+Xzw+XyIRCJYvXo1PvGJ\nT6CjowM9PT342te+BtM0s/sRQuCOO+7A/Pnz0d7ejpNOOgmWtaO7x4033oi+vj7MmDEDP/rRj3KO\noZh9//SnP8X8+fPR0dEBAHjllVew7777wu/348gjj8TQ0FCVn6nCSg5wPR4PdF1vqCidimOHQxD+\ntqo+hmANbt0lpUQcGLHILAqlhA+gSkcX3BZbhVVLJB6HZhll31/t6UXyg02QNaiN5bCH0oXicfjS\nr2+by4lIDcYqU+t54403cMUVV+BPf/oTQqEQnn32WSxevBi33XYb9t13X4TDYYTDYXi9Xqiqihtu\nuAGDg4P461//iqeffho333xzzv5+/etf49lnn8Vbb72FVatW4Ze//CUAYOXKlbjmmmvw29/+FuvX\nr8dbb72FwcHB7P2K2ff999+PZ555BgMDA4jH41i0aBGOPfZYbNu2Deeffz7uvffe6j9hBZQVpZ56\n6qn42c9+hiOOOKLSx0NVIk0TsKyqLzITug5727aqPgaNLxOYusvM4IrOTriCJgzTAjxs+Vdp0UQC\nGsrvjaoGeoF4HPa2rVC7p1fwyPKxRKF0oaQNn0y1BvNrHpgON0zLhJuZ3Ka35aLzII3yP5yOxfvf\ny6GM6lPucDggpcS6devQ39+PQCCAQCCA559/Pu/+e+21V/bn2bNn4ytf+Qr+9Kc/4dvf/nb2+ksv\nvRTTpk0DABx55JF48cUXcfrpp2PFihVYsmRJdu3UddddhzvuuKOkfS9duhQzZswAADzzzDOIRCJY\nunQpFEXB4YcfjoULF07m6SlbWQFuZ2cnnnvuOey+++747Gc/i5kzZ+Y0AhdC4IILLqjYQdLk2ZHU\nmN6qlyhoOksU6iyzOMyT/pO0Y1Go3TOKvr/S0Qn34HoYsSjQXt3zZSqK2jb0RPm9UUV7B+DRkNy8\nsfoBrmAGt1QhCfjSY5jbdB2wgFAoAvc0BrhUvHnz5uGee+7BT37yE5x22mk46KCD8soHMt588018\n61vfwurVqxGNRpFIJLDHHnvkbNPT05P9Wdd1bN68GQCwcePGnG07OjrQ1rbjm95i9j179uzszxs3\nbkRfXx+UEZMaZ8+ejeHh4TKehckpK8C95JJLAACbNm3CunXr8m5ngNt4ZCgEADXI4HrZRaHOshlc\nZUeJQjE9cDOE7oUrEUesCpkKAqI2oMvyx3MKIaD2BGBv2gh8bI+J7zAJXkXBNvZxLUlYKOhC6jlr\n8/mAbVEEo1F0T+uq85FRszn++ONx/PHHIxqNYunSpTjrrLNw5pln5m13zjnnYLfddsN9992HtrY2\n/Nd//RdWrFhR1GP09vZi/fr12cvDw8MIBnf02S5m3yMTnL29vdiwYQNs284GuevXr88JmmulrADX\nZl/EpiPDIQhNg6hy7TRrcOsv0792ZA1uMWN6M4QQcEsJ0zAn3phKFoGALif3HqoGepGsQScFXRF4\nP8EMbinCDidmJ1MlKC6XC25zG4KTqLmmxjFj2c0Tb1SG0eUJQKoG97333sOBBx4It9sNn88HVVUx\nc+ZMbNiwAaZpwp3upR0KhdDW1ga/348333wTt912G9rb24t67C9+8Yv48pe/jJNPPhnz58/HpZde\nmpN9LXXfBxxwADRNww9/+ENceOGFePrpp/H444/j+OOPL/FZmbyy+uBS86n2FLMMRUtlcGuxAIYK\nM6WECsCZCXBjpdXgAqnyBoMjRqsiqiiYbM+ZzMjeakuVKPBvuRRhlxs+x47etz7TQNAqv+aapibT\nNHHZZZdh+vTpmDZtGp577jncdtttOOSQQ7DnnnsiEAigo6MDkUgEN9xwA+6//374/X6cccYZOO64\n44p+nIULF+KSSy7BkUceif7+fsybNw/d3d3Z20vdt9PpxMMPP4xf//rX6OzsxI033oiTTz657Odh\nMspK5z3zzDNj3qYoCtrb2/GRj3wk++mC6q8WU8yA9DhYKSFNo6SsIVVOashDOriVsuQSBQBwKwqM\nRKIahzflxVQHdEwyg9vTC3twC2QiDuFwVujI8nkVtgkrVdjtQZvY8eHQZ5kIJ8svSaGpaffdd8df\n//rXgrdl2rRmfOpTn8Lrr7+ec913v/vd7M9y1ELR6667LufyhRdeiAsvvDDncrn7BoA999wTq1ev\nLnjstVRWgHvwwQfn1FxIKXMuA6n+uGeffTaWLVuWk+6m+qjFFDMA2UBKRqMAA9y6SI3pTV+Ix4FE\novQMrsOBbRYzuNUQdTihY3IfHpRAALBt2Fs+gNq7U4WOLB+7KJTGsiyYbg/alB2vry8RRwh8Dolq\nrawAd+XKlTjzzDNx6KGHYtGiRZgxYwa2bNmChx56CE888QSWLVuGV155BcuWLYPP58PVV19d6eOm\nEtU0gwtwoVkd5UwxS3e0KDmD63LBYoBbFTGXC15M7rlVdC9EWzuSmzdVN8AVApYE4lJmS15obMFw\nGICS6p6Q5pM2wnzuiGqurAD3jjvuwAknnIAf/OAHOdcfddRRuPTSS7FixQo8+OCDsG0b9957LwPc\nBiDDYSgltIoql3C6AIeTrcLqyJRyR4lC+oNGoUUM4/G43TAiMUjbhuA3MBVj2zZibg+8k+iDm6EG\nql+H601/UIrZEk6VQdpEgtEo4PDB79/xjYlfAO8qHIxEVGtl/cv12GOP4ZBDDil422c+8xmsXLky\n+/OGDRvKPzqqmFqVKADspFBvuWN6o4CiAB5PSfvQNA8spxMy3T+ZKsOyLCRVB/QKrE+oxchePR3g\nRlimUJRQzIB71FAHv6og7KxenTQRFVZWgOvz+fDkk08WvO3JJ5+ELz1MwLIs+P21CapofDIUgqjR\na8EAt75GLjKz0y3CRtfIT0TTvTBdbth1miHeqsLpvwtfBca3qj29SG6uboCrCQEBcNhDkYJWHF4j\nlnOd3+lE2FXaB0wimryyvjc555xzcPXVV2NgYAD/8R//genTp2NgYAAPP/wwfv7zn+Oqq64CAPzl\nL3/Jm3hBtSdtGzISrmEG18sShToypIQ7Hc+mxvSWvtjP7XTAcrlhD38A9M+e+A5UlHDMAFQdXn3y\nAa4S6IUcHoKMxSC0ye+v4GMIAU2wk0KxQokEfDK3f3Sb242oW0MymYSqqmPck4gqrawA94orrkBH\nRweuv/56/OxnP4MQAlJK9PT04KabbsJ5550HADjppJPwla98paIHTKWT0QggZU0WmQGZcb3M4NaL\nKSV8ZU4xy/AIkcrg1mG8YiuLWiYUl7siLRTVmT2AEEh+sAmOOTtX4OgKYyeF4oVtCd+oDhl+TYMN\nFZFoFG38RrOp8Rvp5lJ25fs3vvENnHvuuXj//fexadMmBAIB7LTTTjktwebPn1+Rg6TJkeFUHWWt\nAlxF19lFoY5MKTFNpP4OZay0KWYZbiGQVFXEtw+BX65WTsSyoEmjIq0ThdMFZVo3kps2VjfA5bCH\nooWEgN/O7Xnb5tOBcBKhSIQBLlENTWppp6Io6O/vR39/f6WOh6rADgUBVa3a15ijCV1HcnCwJo9F\n+Qx7RBeFaOlTzABk24wZoRD4T3LlRONJ6LJyI5BTI3urv9CMNbjFCSsq+u3cDK6u6VC2DyNoxdBX\np+MimorKDnD/9re/4Xvf+x5eeOEFvP/++1i1ahX23ntvXHbZZTjwwANxxBFHVPI4aRIyPXBLXWhU\nrlQN7r9q8liUz5TI6aIg2tpK3kfm/rEIM/GVFEkmoU2yB+5ISk8vEm+/VbH9FeJVBLsoFCnsdMGf\nyM12q6oKrxFDMF65DzZUH+FwuODkrsli6UN1lPU92cqVK7HXXnvhX//6F7785S8jPmJmvdPpxE9/\n+tOKHSBNngyHoaQ7W9QCa3Dryxy5yKzsEoXU/41YbPwNqSRRW0JPVm4EstrTC3vzpqr8o5vBDG7x\nwi4P/M78vJHPMhCKc/R1s6vm3xlVXlkB7iWXXIIvfelLWLVqFa644oqc2/baay+89NJLFTk4qgw7\nHITwlZ7FK5fQdcgYuyjUiyElPCMmmSnllCikM7hGPA6Z4ESzSokC0GXl6lnVQAAyGoEMBSu2z9FS\nNbj8h30iyWQSEY+GtgILCH1xC6Ek65iJaqmsAHfdunU4+eSTASDva++Ojg4Msv6yochwGKKWGVxd\nh4wy81cvIyeZ2WV2UVCFgBOA5XKxk0IFRYUCvYJZIGXadMDhqGodLrsoFCcai8JWVbQVWOvgs5MI\n8ykkqqmyAtyuri5s3Fj4DfXNN99EIBCY1EFRZdVyihmQqsFFIg4Zn/w4UiqNLSWsEYMeyi1RAFL7\nMN0e2MMc9lApUVWFXsFSeKGqUGf2VHWimZd9cIsSDKfKsvze/G9M/FIipLAHLlEtlRXgHnPMMbjy\nyivxxhtvZK8TQmDz5s244YYbsHjx4oodIE1eZpFZrWQCKtbh1p6ZjkM8QkAmk4BhlJXBBQCPImC1\nd0IywK2YmMMJrzr5FmEjKek63GrRFYUlCkUIxWJQ7CS8Bf7efIpAWJ1U0yKiprZkyRIsXbq0po9Z\n1jvttddei+nTp2P33XfH/vvvDwA4/fTT8dGPfhTt7e3ZSWbUGGQoBKWGqzQzNZ+cZlZ7ZvqrZLdA\nthdxOW3CMvuIt7Uzg1tBUacLuqOygY7a01vdDK4iEJMSNssUxhU0TeixWMFpZX6HA2HX5Id70NQy\nZ84cPP744/U+jKZV8jutZVl44okncN999+H//u//sHLlSnR1daGrqwtf//rXccopp8DlclXjWKlM\ndo0zuPB4AEVhBrcOjEyAq4js819uBtctBKy2dtib2PKtUmIuD7yysqU7ak8Axh83Qdo2RAUGSIym\nKwISQExKeGvUarAZheJx+KRR8LY2lxMRd236kBPVWiKRgKPCH9wroeR3Q5fLhRNPPBEbNmzAaaed\nhvvuuw9//OMf8atf/Qpnnnkmg9sGI00TsKzaligIAeHRYHOaWc1lMrgeIbIZ9HJrcD1CwPJ6YQ8x\ng1sJ8XgclssFvQJjekdSA71APA5729aK7jdDTwe1LFMYXyhpwxcv3HHEr3lgutwwLfbCpeKccMIJ\nWL9+PY499lj4fD5cdtlleOutt3DEEUegu7sb8+bNy2nJetVVV2Hx4sU4/fTT0dbWhg9/+MN4/vnn\n8Ytf/AKzZ89Gd3c3brzxxpztP//5z+Okk06C3+/Hrrvuiqeffjp7++bNm7F48WJ0d3dj7ty5+MEP\nfgA7PdHwqaeeQk9PD2666Sb09fXhqKOOyh5zIBBAe3s7DjroILz66qs1erYKK+vj/vz587F+/fpK\nHwtVgR1JjemtZR9cINNJgQFurZm2hILUVzMyFgPcHogCX5kWw60IWJoX9vC2ih7jVBVJf+Dzeiob\n4Ir2DsCjVa1MQU+3nGMnhfGFJOC3C/e6bUt/ixIKsWyLirNixQr09/fjoYceQjgcxmWXXYZ///d/\nx9FHH41Nmzbhsccew3XXXYeVK1dm7/O73/0OxxxzDIaGhnDMMcfguOOOw1/+8he8/vrrePTRR/Gd\n73wH7733Xnb7Rx55BAsXLsTQ0BAuvvhiLFq0CEPphMYJJ5yA9vZ2rF+/HitXrsTy5ctx1113Ze87\nODiId955B2+//TYeeughAMDhhx+ON954A1u2bMF+++2HE044oUbPVmFl5ZSvvfZanH/++fi3f/s3\n7LPPPpU+JqogGQoBQG1LFJCZZsY381oz0h0URDqDW255ApAuUfBosIeHIKWs2SS8VhWJGgDc8OmV\n/apaCAE10At780Zgt1T+aQsAACAASURBVD0qum8AcAoBlwA7KUwgLBR0oXCv2zafD9gWRTAaRfe0\nrhofGVXKeeu3ZMvAKmn5Ll7oEyw+/d3vfodAIIBzzjkHAPDRj34UZ511FlasWIHDDjsMAHDAAQfg\n6KOPBpAKUG+44QZcddVV0DQN+++/P+bMmYO1a9di1qxZAIA99tgj2/L11FNPxU033YRHH30UBx98\nMJ5++mk8+OCD0HUdH/rQh/Dtb38bv/jFL3DWWWcBSA29uPbaa+HxeLLHeNppp2V/vuKKK/DjH/8Y\nW7duxbRp0yr0TJWmrAD34osvxtatW7H//vtj2rRpmDlzZs4/fkIIrF27tmIHSeWT4RCEpkHUuD6G\nGdz6MKWEJzPFLBqFMokA1yOAYZcbME1II1Z2qQOlREwDcLuheypfi6n2BKq60IzDHiYWdjgxO1m4\nvtrlcsFtbkPQKlyjSzSRd999Fy+99BI6Ojqy1yWTSRx00EHZyz09Pdmf9fR7/+jrwuFw9nJ/f3/O\nY8yePRsbNmzAhg0b0N7ejs7Ozuxtc+bMwYYNG7KXu7u7s4+ROZbLLrsM//u//4uBgQEo6fUAg4OD\nzRXgLliwgJnbJlHzBWZpzODWhykl3EpmyEOk7A4KQCqDazidAAA5NAQwwJ2UiGnBAwOqo2PijUuk\n9vTCfPaZiu83w6uwF+5Ewi43fPHkmLf7TANB9ganEoxMHPb39+MTn/gEnnrqqYrtf3Sp6fr16/GF\nL3wBfX192L59O4aHh7MB9bvvvou+vr6CxwYA9913Hx588EGsXLkSc+fORTAYREdHR13HG5cV4N59\n990VPgyqllr3wM0QmpYtj6AdpJRAMgHhcFZl/4aU8FRgyAOQWmRmCgVwe2APb4Pa2zfxnWhM0UQC\nmqjOIiO1pxf24BbIRLwq55YmUq3CaGxhtwdtonANLgD4LBPh5NgBMDW+m/tnVGW/Y5UnzJw5E2+/\n/TYA4KijjsLSpUtx11134aSTToKqqnjttddgGAb23Xffsh537dq1uO+++3D88cdjxYoVePvtt/G5\nz30OXV1dOOigg/Dtb38bN998MzZu3Igf/ehHuOiii8bcVygUgtvtxrRp0xCLxXD55ZeXdUyVVPme\nMpT1RMTA+2bhVbW1YodqO8UsQ+jebB9W2iH+4gsI3fjDqu3ftHeM6ZVljunNcAkBS0ooHZ0c11sB\nkUQSulWdDJ4SCAC2DXtgS1X271UUZnDHYVkWTLcHbSPqEUfzJeII8UMCleCSSy7BD3/4Q3R0dOD6\n66/HypUr8cgjj2DWrFmYPn06vvKVryAYDJa9/6OPPhqPPfYYOjs7ce211+Khhx5CV1eqRnzFihXY\nunUrdtppJxxyyCE45ZRTcOaZZ465r1NOOQU777wz+vr6sMsuu5QddFdS4zUuqwOXywV3hVv3AMCq\nrWFMsxI4qIZDFkYzwyGoM2bAX+tj6OxC5I2/V+xxM1+HeL3eun7lMVlbXlwNe+AD+LzeqvQslUYC\nXijw+/0wLAvOcV57h8Mx7uvTIRVYMQuu7m44ouHan0NNothz0xQCup2oyvMofT6EPB64DQNaFfbf\nHo0jLmXNzoGJzs1G88HAAABgZve0MY+7TQEiUq3p79Uq75tT1aJFi7Bo0aKc6x5++OGC244esDV/\n/vy81/zll1/OuexyufDLX/6y4P56e3uz3RFGO/jgg7F58+ac63w+X96xnXLKKdmf6/HNPwNcpD59\nW1XIrLQLYMCyEAqN/bVVtVmDW+Dsn41QjcsFLFVFMhSu2OOqqgqXy4VIJIJkk37NZwe3w3zzNUBK\nBDe8D6Wjc+I7lShoWnBIiVAohHhoO6DOHfM18Pv9478+poVY0obtb4PxwQc1P4eaRbHnZiiRhCZk\n1Z5H0d6JyKaNSMydV/F9u5IJbE4ka3YOTHhuNpjNg4OAwweHqox53F7bxoDirOnv1QrvmxOpRnKK\nWgNLFKqoU1WwNV6/4BYA7OFhKO2VD6QmIjQddoyLzEaKr30x1bNUiKqNvzVlZUsUzGyJAoc9TFZU\nCOiycBupSlA6O6vWs5hdFMYXihlwWybc44zj9asKws7q1N4TUT5mcKuoUxH4V6J+n5pl3IIMh6B0\n1iHA1XXAMCCTybIHDbQa66U1cO29L6wXnqtawGhIic70goVUgFt+FwWPEIgDkAxwKyKqqOgaYxBA\nJSgdnVWbOqcrgoMexhG0LHhlbNxt/E4nws6xa3SJaml0SUMrYga3ijpVBVvrGOBmFgYpHbVvLJ7J\nHEpj/Df9qSK5dRDJ9e/CtdeCqgYipp3qoiClhIxNtg9uKhNsdXRBbh+GtKuXfZwKoqojOxWsGqqZ\nadcVZnDHE0ok4ZtgDG+b242oprVsqQBRo2GAW0WdqoJtiUTdivvt4SH8f/beNUaS7C77fM6Ja0Zm\nZGVWVXdXZg09M9gYfBl77G6hRbAg8cFeeD9YYAkkVl4N7Gr5YmRZQsIC82Lti3exAC0Ce5eRtWsk\noxfEmg9oBSvwsryLZMHuTs/FYxgYwJdxV1Z11yXvkXE9Zz9ERnV1dV0yI05cqvL8voy6JzsyKjMq\n4jn/8/yfPxQFpOAxvQBAanHlUGbhxgSvvAS61YHS2QZtr+e2lezNJ5nBcwHGMsWEJXm6fqsNMAY+\nTt+tKwFmmo56jrsZ8XWVj8Ctzy0KslHpbCaMw44uTsyxazUwqmAqB+BIJIUgBW6OrCsUAY9vfmXA\nBn3QVjuXbv3LOK7gyps5gLk94f3xcJQ8Y7dczmEQgM0/92yjeuP/BvOYOdbPR5SvCo6uw9Lyc4WR\ntVZulXaLEoQAfKlvz2RMCBqXfO5NO170j6dy0S+RFIH04OZI4oU8Ygz2JXOm84D3+7l06i8CURTA\nMKXABRDt7oA92IX2/B0AAGm1wf/tzVzey5sPeuDHAjebBxcAfKqgbjelDzcDURTBNUw0eH6TrGh7\nPa60j4Yggn/v6/NFssM5DORns7iqTKiC25f4qy2zBjoYYOTPIEemXE2uUnSdRFZwc0UnBA1K0Y/K\n8S6yYV/4g24ZiGVJiwLi6q1y+xkoG5sA5t3ueXlw56N6uTMFVBXI0LWtEAIVcVVYJilkY+a64ITC\nMvOLNKJra7kldFjzxY4c9nA2E02/tIihKArq7gwjL59pdhKJ5HGkwM2ZDU1BPyrJotA/Kq2CCwC0\nZq38NDPOOYJX70F//53jv6OtNrgzBRecvcw4h8dxXMEllvXEvPBlMZOosBz9navAdBY3WzZqtdze\ng6gaSMPO5XsySPywcGSj4ZlMdBPNBRaTDd/FuOToSIlkVZACN2fWVaW8Cu6gX0pEWEJcwV1tgRu9\n9S2w/hG0933g+O+SRYdoIZL4Iw0SN/dlaTBLMCiBxwHaauVWdV4Fpq4LALCs/AQukF9UGCFERoWd\nQxRFmJo12IZ+6WsbgY9xSc8DiWTVkAI3ZzZUtRSByzmPBW4JQx4SiFU/bnZaVfxX7kF92/eANteO\n/47UG4CqCk9S8ObiwyAEfJYtAzfBILFFgbTWwXNKflgFpp4HLQguHAQgAtpeBxvmm6QgeRxn5oAp\nCpoLVOcbLMJEfoQSSSFIgZszG5qCfgnbenzmAL4fN56UxKp7cDljCF57Gdo8PSGBUJpLkoI7F7gn\nLQpZMQmBx/h8SlY+yQ+rgOMHMH039/fJs9JuUSI9uGcwmsSLeLt++YLS5hxjKgffSCRFIAVuzmyo\nKo5KqOAmDznaahX+3gmrblEI/+1fwJ0ptOeef+L/0bW28Nit4woujSu4VIBF4Xhc71obfDoR7hte\nFaZhBKuAzy7PSru0KJzNeDYDZRHqCywoG5RgosjwIomkCKTAzZl1VcGYcYQFPxj44AgwayBmvp6/\niyC1OvhsdSu4wSsvQf3ed545TYy02+CCPbgu4yAANMw9uAIsCiYhcYpCOx/f8KrgsAhWePEgABHk\nWWm3pEXhTEaeB2s2g7LAEA9bVTHJ2aYikUhipMDNmQ1VAQcwKLiKW3aDGZBUcFdzVC8PAwSvv3o8\n3OE0ecRuxQkKcUMQE2RRSJrMHvmGpcBNg8OAWpR/93yelfa6HNd7JuMgQGNB+0lT1zAtseggkawS\nUuDmTEtVQAH0C34wsBKHPCSssgc3/Oc3wMMI2rueO/P/5yNweTymF7EHW0SKgpk0mVEKutaSAjcl\nDoA6z3+Rm2el3aIUU2lReIJxxGAHiy0o7JoJTzfg+TILVyLJGylwc4YSgjVKCk9SSMb0lgmZ5+Cu\n4vx6/5WXoL37ORDj7O3IZNiDyM/GPSlwRVVw501mAEBb63Jcb0qmVEGtgAFgeVbapUXhbMY8TkdY\nhLV5I9p4vJoLf4mkSKTALYB1pfhpZlUQuNSqA4wBXv7d41WCex6Cf3j9seEOp6GtdSAMhFa4Pc5h\n0kTgivHgGvMmM2DuGx7KJIU0zBQF9YxDNxbhUUKHeIFbpwRTOejhCSaUooHFhH+StDBa4eZbiaQo\npMAtgLZCC09SiD245UWEATiuIK5aFm7wD18DUVWo3/vOc19zPOxBYKSTx+cZuIEPBIGwmLAkfiwe\nIiAruGlwVA0WLeZ2m0dCByBTFM5jomiwF1y86LoOw3Mxcldr0S+RlIEUuAXQLriCy6MIfDgovYKb\neEBXbVyv/8o9aO99P4h6/uhOYhggNUvosAeXcZgEx9FsQiq48yYzIB/f8Kow0wzUtWLyT/NI6ABi\ni4LHgUiK3MeY6AZsdfHvtuG5GMm4PYkkd6TALYC2Qgsd9sDHI4BzkJIFLnQdUNSVysJlzhThm29A\ne/58e0KCaCGSNJnxWZxcIWRUL3mUr5uHb3gVYIxhZpiwtPMXPCLJayFize0v0of7OBPDhK1fPqY3\noeF7mISLeXYlEkl6pMAtgDYttoLL+kcAIaBr5Q15AOK4qlVLUgi+9ipIvQ71bd9z6WvjLf8cBK4z\nBQgBMc3MxzQJgXuiyUy0b3gVCIIAoaqifk7DoWjy9OACkEkKJ/B9H55hornE71ojDDCWn6FEkjtS\n4BZAW6FwOTArqPLBBn2Q5hrIAsHjebNq08yCV+5Be98HQBbwW4oWIu68yYw7DkitttA5XIZBCALE\n29J5+IZXgcn8+q8LWHAsQl6V9hqRFdzTjCYTAEBzCb97gzOMUUCkhkSy4kiBWwDrSnwzK6rRLE5Q\nKLd6m0Cs+spU/NhwgPAb/wL9+bOHO5yGttaFenCTJjMmKEEBwHHsmM/z8Q2vAtO5ZaQhoOlvEfKq\ntCuEoEaAqRS4xyRpCLa9+O+bTYAJleN6JZK8kQK3AGqEwCDAoCAfbjzkodwEhQRSqx17Qq87/muv\ngK5vQLn99EKvFz1W1WUcxrzJTIT/FogHPQA4TlLIq4HpOuN4PiiLYBZVwc2x0m5RipncXj9mPHNh\n+B6MJcbv2grFpCA/tkSyykiBWwCEELRpcVFhbHBUeoJCwipVcINXXoL2/AdAFowMoq02+GgIHolp\nOPE5h0kI+GwqJCIMiFMUgBONZmtifcOrwMT3YXoeaEExYXlW2i1CZAX3BCPfR91dbgFvaxomejGL\nHYlklZECtyCKjAqrwpCHBLoiHtzoYB/Rd769sD0BmFfaOAcTNDzBPW4ymwkTuOZcrB83mrVlVNiy\nOGGIWsGjWXOLCqMEjhz2cMw4jGAv+d02DQNOrYZI0MJWIpGcjRS4BVGkwOWDPki7GgKXWHWwFajg\nBq/eA93qQOl0F/43pLkGECJMiDyKCZuC1MR4cFVCoOBEBbe1LgXukkzDCFYYFPqeeVXa65TIFIUT\nTBhHI1ruu7VrNTCqYLoCC3+JpEykwC2Iosb1cs8Dd5wKeXCtaz/ogXMO/5WXoL9/8eotABBFAWmu\nCRGMnHN4HI9SFAQ2NBnk5LCHlhS4S+JwDisKC33PvCrtFiEyReEEY0LQWLKi3Zw3pI2n13/hL5GU\niRS4BdFSKPoFPBiSh1p1UhSuv0WB7fbAHuwtNNzhNLS9LmSsqs8BDsybzMSlKABxo9nxuN72ulDf\n8CrgcMBixX5eeVXaY4uCFLgJE6qgsWTil2XWQKMIoxVpvpVIykIK3IJYpwSDiIHlvL3HBn1A1UDq\njVzfZ1FWQeD6r74E5fYzUDY2l/63cRZudg9uYiGIPbgOaK2W+ZgJ8bjexKIQ+4b5cCjs+Ncdh1BY\nvFjfam4VXErgSIvCMRNNR1NZ7jGqKArq7gwjr1hftkSyakiBWxBthSICMMq5+sEGfdB2e+FO/rwh\ntToQ+OBBsR7EouCcI3jlZejvX756CyQCN3sFN6mwxikKjtAKbmxRmMeEzX3DMgt3cWaKAqvg30fR\nCR0JdZmi8BgT3YSdIvKr4bsYB8XaViSSVUMK3IJozVf5eftwWb86EWEAjr2g19WHG337m2CDI2jv\n+0Cqfy9qmlkiQHVw8Jm4FAXg8XG9In3Dq4KjqLCWrPJlRXRCR4K0KDwiiiJMazXYhr70v20EPsYF\njm+XSFYRKXALQiMENiW5C1xeoYgwAMeVxOtqU/BfvQf17e8Aba6l+vdUUJyTxwECQJsvJMRXcB/9\nWZRveFVwNB31gsdmi07oSKhTCofz3K1WVwFn5oBRBc0UdqAGizCRH6FEkitS4BbIukLRzzlDkg36\nIFUSuKYZP2ivYVQYjyIEr72cqrksgbTWwWcz8CXD4k+TTDHDvHFFbIrCowoxIM43vCrMDBNWwZOr\n8qq0W4SA45ElZpUZTeLFZLOx/GLS5hxjWuyiRyJZNaTALZAWzT8qLPbgViMiDAAIpSBm7VpaFMJ/\n+xdwx4H23POpj5GkXWQVjI+GPMwruIJG9QJx9Jj7hMCVFdxFCMMQnm6gri+/jZ2VPCrt1nyynbQp\nAOPZDJRFsFL8rtmUYKKoOZyVRCJJkAK3QNYViqMovwcD53w+xawaEWEJxLLArqFFIXj1HtTvexdo\nhmopseqApmeutB2P6XWmgK6DqOIeniebzABxvuFVYDpf2NVNo/D3zqPSfixwZQUXI8+DNZtBSWE/\naagqJnrx14REskpIgVsgbSVfDy6fTIAwrMyQhwRi1a+dRYGHAfyvvQo9gz0BAAgh80inbJW24wqu\n4AQFIG4y805U7ET5hleBqeMCAOoCY9sWJY9Ku04INEAmKQAYBwFs3031b5u6hqlZ/DUhkawSUuAW\nSDtnD27yMKtSkxlwPbNww396A4giaO96LvOxaCv7WFWPx3m1oqeYAXEF1z2hZ0irLcQ3vApM3bnA\nFfydLEJelXaZpBAzjhgagZ/q39o1E55uwPNlFq5EkhdS4BZIm1JMGIef0/YeGwxArDpICX6/i7iO\nAtd/9SVo73kOxMi+zShCiHicz6eYOaA1sRXcs5rMgOy+4VVg6vswfA+qQMvIouRVaZfDHmLGPE5D\nSMNafT6ud3y9drYkkiohBW6BrM+zMAc52RT44Ai0Xa3qLRAPe7hOTWbc8xD8w+vQn78r5Hi0tZ5Z\niLgs9uCy2VR4Bfd0k5ko3/Aq4AQBal66beys5FVpt4is4ALAhFI0kO5zsOcCd3TNFv4SSZWoRBvn\nZDLB5z//ebz88suo1Wr4iZ/4CXz4wx8+87Vf//rX8fu///vY29vD7du38Qu/8At49tlnAQB//dd/\njT//8z9Hr9eDaZr4/u//fvzsz/4saiX4386iQQkUxMMebqriI2JYvw9SMf8tMG8yO3hY9mkII/iH\nr4GoGtTvfaeQ45FWS1AFNz+LwskKLiFEJiksiBNGsJBuGzsrJyvtypa4e2CdymlmADBRNDwdpftu\ndV2H4R1h5Eubj0SSF5Wo4L744osIggBf/OIX8elPfxpf/vKXce/evSdeNxqN8JnPfAY/+ZM/iT/6\noz/CD//wD+PXf/3XEczHwHqeh5/7uZ/Dl770Jfzu7/4udnd38cUvfrHoH+dcKCFoKxRHOVVwq5ig\nAFw/i4L/ykvQ3vu8sKQC2l4HG/TBM/izT8aEiYwIA2KB63M8Fu5P29l9w6vAlDHUonJGsuZVabfm\nwx5WnYluwM5QqGh4Lkb+9RxhLpFUgdIFruu6+OpXv4qPfvSjsCwLzzzzDD74wQ/iK1/5yhOv/bu/\n+zt0Oh386I/+KDRNw4c//GFwzvHqq68CAH78x38c73nPe6BpGprNJj70oQ/hjTfeKPpHupC2QjHI\nqfoRC9zqVXBpzbo2FgU2nSD85zcyDXc4DW21gSiKUzBS4vHYSpBXikLyHgkyKmwxHA5YUTqfZlZE\nJXScJrYoyDGzE7MGO0O/Q8P3MAnLuTYkklWgdIG7s7MDzjmefvrp47979tln8dZbbz3x2rfeeuvY\njgDEN/BnnnnmzNcCsZ3h9u3b4k86A21KcqzgVtSDe41iwoLXXwVpNKC+7XuEHfPRsIf0QuRRk5l4\nD65BHr1Hggjf8CrgEAKLlycGRSR0nEZaFADP9+DpBpqmmfoYjTDAWFbCJZLcKN2D67ourFMP5Hq9\njtnsSW/SbDZDo9FY6LV///d/j7/927/Fb/3Wb4k94Yy0FYpvB+JX7TwMwcfjykWEAbFFYcqBqN8H\n5tXAZaGEVsJLPXvtFejv+wAIFbc2JJoOUm/EQuT2M6mOEY/qzcmDS5MK7gkfbquF6OgQbJgtSYHU\nG5msHiHnUBAvdquIQyhaKK9Kl0elXaYoAOPJFABFs57+d63BGcYVvW4lkutA6QLXNM0nBKrjOGeK\nmVqtBueUl3M6nT7x2ldffRW/93u/h1/5lV9Bt9t94jgHBwc4ODg4/jOlFDdu3MjyY5wLIeSxSTcb\nmorXvDDV9JuLiAZ9gHOoG5vCj52V/dYG/vuP/QqQKfKR4bv//h4++YHsubNpmUwm+O9+7KfwceLj\nuwV/xrS9DoyGqb87jwM1SsGdKdRGY6HjnL42z6M+F/MBocev17Y6mPWPMPoPn0p1vgn6++/C/q/+\n69T//n94MMB/Yddw1ypvKlTymZz1Wc5UDfWIl/Y7qaxvIPjXN4W+f0NR4LB8f6ZFr82ymM5cQLHQ\najZTn2eTEnyTqrn+nBddm0UxdRz85T+9iQ997ztQz7AgkEiWpXSBu729DSC2HyR2gm9+85tnWgtu\n376Nv/zLvzz+M+cc3/rWt/BjP/Zjx3/32muv4Td/8zfxS7/0S3j3u9995nv+6Z/+Kb7whS8c//mF\nF17Axz72MSE/z1noJ3xat9Up+kMHrVZLaNVp9mAXA0qx8fQzQquLIniDqmjuPMSn19N7Q7/27bfw\nH5st1Ov10m7Wb+09gK8b+If2Ju4ItoK4N29BnU3RTnlcf7ePDdMAGMParQ6MBY+jL+Ah5JxD6R1B\nq9fRbswfUO27WP8f/yfwMH0D1eT/+3uM/u//K/XP7DKGnZ1D7FIl9TFE0mw2n/g7R9PQNso7P2X7\nKRzd+3+Fvv8tzYEzSH+tLsoi12ZZeG99B4bvYWvr7amPsVEz8XVGC7k2zro2i+I7Dx/i/7yxjf9y\ncxNmCSOrJatL6QLXNE384A/+IL70pS/hE5/4BPb39/FXf/VX+PjHP/7Ea3/gB34Af/AHf4C/+Zu/\nwQ/90A/hL/7iLwAAzz//PADg9ddfx2c/+1n84i/+It773vee+54f+chH8CM/8iPHf6aUop9TR3i9\nXsd0+sh/qgUhPM5x//AIDUWcEPXeegt0rYXBcCjsmKJ4c+SgoymoZRiK8PbNDfgzjm9861vYXC+n\nke7NBw+BzS6+Opzg39U0UIELlKjeQLC3m+o65JxjxhiCfuzhHUcRnAWOc/ravAiDEByMxugHJ8vw\nBFC1pc83IehsI3j4AEd7e6kGZnxr3oH+jYmT2+/vIiiKgmazidFohOhUQ5mjGVDDaWnnF+gGwqND\nHB0eClv4Mj9EwDkeHB1Bz2mLfZlrswweDodoqLVM36vJOcaakeu1cdG1WRRv7j3AhlrDbOZglkOz\ncRUWt5JqUrrABYCf//mfx+c+9zm88MILqNVq+MhHPoI7d+Iu9Z/6qZ/Cr/3ar+Hd7343ms0mfvmX\nfxkvvvgiPv/5z+P27dv41Kc+BU2LH7J//Md/DMdx8Bu/8RvHx75x4wY+//nPP/Z+m5ub2NzcPP7z\nwcFBbr/8nPPHjp2sow+CADWBH3/YPwRptUu7iV3Ejh+go9BM59ZutWAM9/DtyRjttTWBZ7c4O0GI\ntx0+xDc3buKfZx7eYaQXd0+w1kL0rW+k+ox8zsEBqHP7DjfMhY5z+tq8CIMAThQJvb7IjVsA5/B7\nO1BvP335PzjFd7xY4O4EYSWu++jU5xNFEWaGCQteaefHm2tAFCEcDkEFVfHMedPcOAjRErhIP8ky\n12YZjIIQDZbte23oOhyjBt/3c9+VOn1tFsmOH6ITTCv9fUquJ5UQuI1GA5/85CfP/H9/8id/8tif\nn3vuOXzuc58787Wf+cxnhJ+baGqUoEYI+hHHdwnUR6x/VMkGMwDYDRmeyygGKaXYGg+xE3l4XtB5\nLcuepuN7QxeGruKeGwgVuFninLx5R7s+cwBFAXLY2jXo48MeREBME7S9gWivl0rg7oYMmwrFQcQw\nZgx2xaw5nueBU4q6gHHOaTmZ0CFK4Cae7CnjaFXXJpsrE8bRQLYM26ZVA+MKpo6Dpm0LOrPqsafq\n+J6UAzEkkixU64mwIrQVgr7gqLA4A7d6AtdjHAcRQ1fA5LbtKECPldN1zBjDnt1Cp2bibk3HK26A\nUKDgo602+HgMHi7/0EzG6BqzOEEhj0QBkzw+rlcUdKsDttdL9W97YYTnTQ0KgN0ckkmyMpk3z9ZL\nTP94LKFDEAYBCIDZCicpjAlBI2MWsN2IexLGFbZiZCW+b66hW0sfpyaRpEUK3BJoKxR9wUHpVRW4\nu/Mg844Agftdho49sxyx0B8O4Romnmq38V5DQ8A5/skTN6Hq5FjVZUkqq5ozET7FLMEg5LhSLBKl\n00W0t5vq3/bCCE+pCm6qFL2weoMHpjMXANAoOd6OtttgQ3EClxICixBMV3jYw4QqsDOuIy2zBhpF\nGJ0Rc3ldGI5GUATp3wAAIABJREFUmJk1bLerN2FTcv2RArcE1inNp4JbQbN9L4ywodDjLNUs3G6v\nYd9uHY9mLpKdg0NoQYAb6+swKcF7TA33XHHbbsRuAoqSKrPUnetObToVPsUswSDksUlmolC2Ooh2\nl6/gjhnDmHFsqwq6qoJeBSdCTT0PShhCKzkNgLba4IIbmSxK4KzwsIeJpsPO6D9WFAV1d4aRlyk/\nsdLcPzyEGga4ubFR9qlIVhApcEugpYgVuHw2A1y3kmN6d0OGrirmMvvuThdMUbB3IsO4KHacGW6O\n+sfNIHdNDa95AXxB27SEUtBmC7y/vA83mWJGnGluFVyTIBeLgtLpgo9HYNPlxhTvBhEUADdViq6q\nHO8UVAknCFDzXNCSvcGktS58XG+dEkxX2aKgm7C17B78hudiHIjbCaoaO1MHN0eDSmcaS64vUuCW\nwLpChY7rTR5epIIWhV4YCbEnAEC73ULDmWBnUHwU2i4HOr57/Od3GRoogNddcdVk0m6nmgzmJVPM\nZuKnmCXk0WQGAPTGLYDSpau4vZDhlkqhEHIscHnFBNc0CGEF5Vfn4mlm2SbOncYiq1vBjaIITq0G\n28hemW+EPsY5jW6vArvs8fumRFIkUuCWQFuhGDKOSNADmQ0GgGGAVGCU7Wl6YSSkwSxhazpGzyu+\nI3dPN9FRHtksNELwPlMXalOgrTZYigquy5MxvVPQXC0K4gUNUVXQG7fAlvThnlw4dVQKlwP9igku\nhzFYKZoGRZMloeM8Vtmi4MwcMKqgKWC3pMEiTK7xx7inm+hIlSEpCXnplUCbEnAAQ0EPiCQiLI/u\n+SwkPkmRArcTBdglxW53hWGIh802tk+NmbxravhHL4QjqNkmrrQt75X0OIdJCLjj5GhRIHBzEjRK\np4NoySSFkwundYXCIMBOxZIUHMZRi8rffs6S0HEeFiVwKlYxL4rRJM6bbjay/67ZnGNMr+f2fRRF\neNBsPXHflEiKQgrcEmgpFAQQ5sOtbILCCZ+kKLZ1DXtWQ9jxFuHh4RFCVcX25uONEu/QVdQowWuC\nbApxBTeNwI2jm5iTo0UhpyYzAFC2lktS4Jxj94TApYSgU0EfrkMIrAqIwCwJHedRJwTTFa3gjmYO\nKItgCVhM2pRgolQijl44Dw8PEaoatmWDmaQkpMAtAYUQrFFxWbhVFbi9kOGmSqEKrCxvN5vo22uY\nFRits9Pvw5o5WLMfD8pXCMH7TQ0viRK47XWwYX9pL6nLOQyasweXIBeLApAI3N7CP/dRxOBxPNa8\nWMUkBYcQWChfBGZJ6DgPi9KVtSiMPR/WbCakcaqhqpjo5Q0CyZOd/gA1d4aWoAEjEsmySIFbEm2B\nSQpxRFj1EhRE+28B4KlbNwEAO/vFJSn0XA+3JqMzu+HvmDre9EOMBHyXtNUCPC9OxVgCjyUWhfxi\nwvIa9AAAtNMFXBd8QQHWCxkMEv8OJVSygktV1CtgG8qS0HEe9RW2KIyDALagxqmmrmFSUrZ33vRm\nHm6Nh6WniEhWF3nllURboTgS5N3k/T7oWvWCtPMQuKZpYn00wM54LPS4F7FLKDrh2c1kz2oKWpTg\nZQFV3CTmjS/ZEORxDoNzwPevXIoCEFeuoesLJykkDWb0hHjsqhR7IRPWuCmCmarBqsjDnbTT+bvP\no0aIMO/5VWMcMTQCMc2lds2ErxvwrmEW7i7IufdNiaQIqnH3XUHagoY9cMbARgOQilVw2SmfpEi2\nZlP0/OKad3bNOrra2T8HJQR3amLSFEitBpjm0kLE5YAxb2bKs8nM4/H3KhpCKZRbnYV9uGctnLqq\nggjAwwpNNHM0HfUcrv80pG1gPI86JZhxVGpBURRjHqcfiGCtPh/XO7l+43p3a/Vc7v8SyaJIgVsS\noiwKfDwCoqhyHtz+GT5JUXR4hD01e8j6Ini+h6PmGrZt+9zX3DU1fDOIcChgi5yuLd9o5nEOfd4h\nn6cHFwCCvBrNOt2FkxTOWjjZCoVNSaV8uDPDhKUXc51ehmiBa80nE85WUOBOKIUt6Oe25wJ35DhC\njlcVfN/Hob2G7WaxDcESyUmkwC2JtkLQj7LfJJOHFm1Vy6Jwlk9SFF1Tx16jmMaF3sN9cEKxfXPz\n3NdsqwpuKRT3RNgUUmwlu5zDmG+Z5lXBNeZ2gNx8uFuLCdyIczwIGTpnLJw6FWo083wPgaahXpEG\norQJHeeReItXMUlhomhoCBg9DgC6rsPwXIzc4ppmi6C3vw9OKbo3zr9vSiR5IwVuSawrFA7n8DI+\nINigD2LbIAVVNBflLJ+kKJ5qtTCt1TEcjYQf+zS94Qit8ejCSCBCCO4KsimkqbR5nMPwPJBaDSQn\nz6eZs8BVOl2wBw/Ao4sF6sOQIQLOtIx0VVqZRjPHiQVL3ayIwE2Z0HEeSQV3FZMUJroBW+DWe8Od\nYeSXPxBEJL3hCGuTERo5Nb1KJIsgBW5JJJXNfsZGDdbvHzcnVYk8GswSbm1uQolC3D84zOX4J9kJ\nQtyaXd7QdsfUsBOyzAKLtteXF7iMQ/fc3BIUgLjJDAD83KLCOkAUgu0/vPB1O2EEmxLYZwj5OCqs\nGh7cyTwJo56TZWRZ0iZ0nIdCCEyClUxSmJg12Hr2Mb0JjcDHpCILM1Hs+AG2nEnZpyFZcaTALYk6\nIdAQZ3pmIc7ArZY9ATjbJykKVVVxYzRAb5p/Y8YeVdFdoKHkpqrgu1QF92bZqrhpxvW6HNDdWW72\nBADQAJD5e+UBtZsg9caljWYXXVddVcFBxDLviojA8TwQzlAzzbJPBUD6hI6LiJMUyv+si8TzPXi6\ngWZN3PfaCAOMr9lCYY+q2GLlT/GTrDZS4JYEIURIoxkfHFWugnuRT1IUW96skGrdbt1G11isWnO3\nFg99yLINTFtt8NEQfMHKPuc8bjLLccgDEF+vJkFu43qBRwMfLuKinYHO/O+rYFOY+gFMzxUyDEAE\naRM6LqJO6cp5cJO0g6bA37UGZxij/LxkkexZDWxXpMFSsrpIgVsiIgQuG/RB2tVKULjIJymKLgH2\n9HyrY+PpBOO6je21xRra7pg6DiOGtzIILNJqA4yBj4YLvT4EwADo00muAhdIxvXmJ2hopwt2icDd\nvWDhZFCCDaUaPtxpEKDmVysDNE1Cx0VYKzjsYTSN0w7shjg7kE2ACb0+43onzhTDRhPdBe+bEkle\nSIFbIkIEbr96Y3ov8kmKomuZeGC3EF3SlJSF3sMDUBZha8FO4JZC8TZNwUuz9A0jdK0FELJwpS1p\n+tKdCUgt34aOvAWustW5cNiDxzgOInah9aWr0kr4cKdRBEvQMABRpEnouIj6Cg57GLkuDN+DITAd\nw1YoJtr1qXb29g9AGEP3xo2yT0Wy4kiBWyJtStDPsMXHAx98OqmcwM3Tf5vw1PoGfF3HocCK1Gl2\nJlNsjIbQtcUbSu7WdLzs+qkHIhBVBbHthYVI4jfVJ+PcK7gmzW9cLzBPUjg6BD9nqlNSme1ccG1V\nJSrMYRxWVC0PYh5ZuCtnUfB9NARHetmahknOu1FFsjOaYGM8hC6wEU8iSYMUuCWynrGCywYDAKic\nBzeOCMv30lpvt2D4HnaO8hO4vTBCx10ugP15U8OYcfxrhklrtLW+8FayN9cX+mhUkEUhv+MrtzoA\n54ge7J35/3thhE2FHic6nEW3IgJ3xgFL0LQrUaRJ6LiIVbQojMMIDV/sWN2mYcCp1XLdjSqSXhih\nM7t+k9kkVw8pcEukNRe4aat9bNAHFBWkUa1pMb3w4m1kEVBKcWs0QG/m5vYee5qODlluAdKgFO80\nVLyUYehDXGlbrNs9qahq42GuKQoAcm8yI6YJ2t44t9FskYVTV1UwZhzjkrfOp4TAqpj4S5PQcRHW\nCqYojDlHIxKbWdu0amBUwfSaTDPbVXV0sFrWFUk1kQK3RNYVihDAJOVDgvWPQFut3ML90+AxjsNL\nfJKi2Ao89HJ6vjLGsGe30E0RB3TH1PGqGyBMKXCW2Ur2OIdOADKdguYcqp63BxcA6Fbn3Eaz3QUW\nTjdVCgXAblBuNWxGFdQq1hi/bELHZdTp6gncCQgaghdPScPauIDYw7xhjOGBvZbqvimRiKY6ymgF\nyTrsgQ8GlfTfAhf7JEXRVSn2jHyqlv3hEK5hYjtFQsV7DQ0B53jDS2dToO02+BJNZgYh4O7syqco\nALEP97ws3GQ63kWohOBmBRrNHFVDvUILT2D5hI7LsCjBtGJV6ryZUAW24IWLZdZAowgjQUM4ymQw\nGmFm1rDdrl42u2T1qNYdeMXQCUGDkNQ+XDY4ih9aFWIRn6Qotht17DdbCALxYy53Dg6hBQFurC/v\nbzYpwXtMLfXoXtJaPM7JYxwmOMB5rpPMgPybzIC5wD0jSWEcMYwZx/YCC6cq+HAdTUddqdbtddmE\njstILAqixv9eBSaaDlvw96ooCuruDKNzmiuvEjuHh1DDADc3Nso+FYlECtyyaWVoNIunmFVP4Obd\nYJawvbkJpijYO9gXfuwdZ4abo37qoP67poaveUGq0ba01QZ3puAL5Kh6HDDm28RFVHD9nLWMstUB\nH4/Apo+P+eyFERTEFoTLqILAnekGrIpFPx0ndAjy4dYpBUN+0+2qyFg3YefwvTY8F+OgWqkbadiZ\nOrg5GlRmwIlktZECt2TWFYKjKH2TGa3YkIdFfJKiaNo2GtMJdgYj4cfe5UDHT9/A9i5DAwXweopm\nsyQVY5FKm8s59Hm3ft5NZkbOTWYAQG/cAih9ooq7GzLcUikUcvnOQFdVsBtGpVUWoyiCa5ioLzgB\nr0hoa/04fSUr1nyXZrYiFdwoiuDUamga4jJwExqhj3HGTPQqsMuy3TclEpFIgVsybYVikOLGxjmf\nD3moYkRYcav3rekYPU98oP6uXkNHSW+z0AjB+0w9lU2BNBqAqi6UpOBxDiMKAU0DybliaBbgwSWq\nCnrjFtgpH+4y11VHpfA4cFSSYHBmcTd8PQchlJVlEjouw5ovNqYrMuzBmTlgVIFdqwk/doNFGF+D\ndcKubqIjVYWkIshLsWTaCsVRigcEd6ZA4FfKorCMT1IUHRZgl4h9vzAMsd9sYbuerSJ619Twj164\n9LQnQsg80unyCq7HOYwwyN1/C8QWhbw9uEDSaPZ4Bbe3xPCQdYXCICit0WzixM1C9RyEUFZEDnvQ\nCaACK5OkMJrEC5dmQ/xOiQ2OCb3a2/pRFOGhgPumRCIKKXBLpk3TeXAfDXmoTrfqMj5JUXQ0FXuW\n2Bzgh4eHCFUV25uLjeg9j3foKmqU4LVUNoXFhIjLOPQgyN1/CxSTogDMR/aeqOAyzpeajkcJKXWi\n2dSNm4Usq4ICd4mEjssghKxUksJo5oCyCFYOViCbEEwUVfhxiyS+b2p4SjaYSSqCFLgl01YoRowj\nWPIhwQdHILUaiFmdh+gyPklRPNVsom+vYSYwYmenP4A1c7Bm25mOoxCC95taqqEPtLWYEPE4YPhe\n7v5bIE5R8Dhy97YmFdzkffoRg8fjWLhFSXy4ZeB4HnTfX2rEc1Esk9CxCNYKZeGOPR/12SyXBqqG\nqmKiV8/Ssgw7/QFq7gxrzWbZpyKRAJACt3SSLNzhklVc1u9XMiKsSP8tAHRvbIJwhp19cUkKO66H\nrckQVECO6R1Tx5t+iNGS3y9ZsILrcQ7Dc3Mf8gDETWYcyD1JgW51AdcFnwuxXshgkNh6sChlJilM\nwxC1ijbaLJPQsQirNM1sHARo5PS9NnUNkwoVK9LQm7nYGou5b0okIpBXYsmsUQKK5RtiqhoRVlSC\nQoJpmmiPR9gZTy5/8YLsEYqtUEy27rOaghYleHnJKi5try+eolDAkAcgbjIDkP80s/Y6oOvHPtxk\n4USW2BnoqBQPQoaohO1zJwxRC8Q3PopgmYSORbAogbMqFoWIoZHT99qsmfB1A94VzsLdBcVWWM3r\nXrKaSIFbMpQQtCjBYMkqSCxwq5OgsKxPUiRbsyl6vrgMyV2zjq4m5ueghOBubfk0haTJ7DI7gMc5\n9JlTiEXBKEjgEkqh3Oo8JnCXva66moIIwMMSGs2mEYclaIEkmmUSOhahTgimK1LBnfA47SAPmvX5\nuN7J1R3Xu1url3L/l0jOQwrcCtBWaMoKbnUazI5S+CRF0eER9lQxEVme7+GouYbtjP7bk9wxdXwz\niHC4xJY5bbWBMACfXlyZdhmH7kwLazIDUGCSQtxolkbg2pTCpgQ7JdgUHM5h5SSEsrJMQsciWJSu\njEVhQinsnK59ey5wR46Ty/HzxvM9HNpr2G6KbfiVSLIgBW4FaKeYZsb6R/FWbkXYnfsk2yWMJ+2a\nOvYaYhobeg/3wQnF9s1sCQon2VYpthSKe0vYFBL7yWWh/B4H9OmkkJgwnQBk/p55Q7fikb0h53gQ\nslQLp7IazRxCYFU4G1ZkVFidkpXJwZ0oGho5jSDXdR2G52LkimuWLZLd/QNwStG9Ie6+KZFkRQrc\nCrCswOVRBD4aVsqDm/gkaYEJCglPtVqY1uoYjrJPNOsNR2iNR0KjgAghuLOkTYEYBohlXbqV7HEO\nfToupIJLCIFB8rcoAHEFlz18gAd+AAagk8Iy0lFpKY1mDqGochLoogkdi1AjZGUmmY11A3aOW/AN\nd4aRX01ry2XsDEdYm4zQKGChLZEsihS4FaCtUPSXqILw0RDgvFIe3DIazBJubW5CCUPcPzjMfKyd\nIMSWMxZwVo9z19SwEzLsBosLLtJqHycJnEXAOSIA+mhciAcXmA97KGBLWtnqAlGIXn8AmxLYKTqz\n4wpu8dXFmaIej7GtIosmdCxCXMFdDYE7NWto6vlFvzUCH5OSkj+y0vMDbDniGn0lEhFIgVsBlh32\nwAZ9gBCQtbUcz2o5yhS4qqrixniA3jR7g8YeVdHh4h8yN1QFt1UFLy1Rxb1sKzmppOquU4hFAShm\nXC8AUNsGqTewM5mmvq66qoKDiMErWIA5qgarBKvOotD2Olhf0LjeFUlR8HwPnm7Arpm5vUcjDDC+\nop/lLlWxxcQ1+kokIqjuXXiFWFcoXA7MFnwQs0EfpLkGkkPgeBqy+CRF0fFmQkaz7tZtdI18qjR3\nahruucHCgxJo6+KosES4Gb5XiEUBKG5cLxDbFHoRSy1wk0zmon24jm6grlZ3KlW8cBoIGdhRpwQ+\nx9KDaq4aSbpBM8ffswZnGKO6lf+LeGA1sK2LafSVSEQhBW4FaCvxTW3RJAXWr1YG7sOQpfZJiqJD\ngF09W3VlPJ1gXLexvZbPJJ47po7DiOHbC9oUYiFyfqXNnWsKPfBBixK482lmRUC3utjTDHRSLpwM\nSrChFOvDZYxhZpioV/hhv2hCxyJYc8/9dU9SGE3jdINmI7+UAJsAE1rdhdF5TKZTDBtNdHO6b0ok\naZECtwLU5s07i9oU2OCoUgK3F0apfZKi6Fo1PLRbiKL0Yqb38ACURdjKqRO4pVC8TVMWTlOg7Yvj\nnI4tCmEIGPltnZ6kqCYzAAi72ziyGpmsL12VFurD9TwPTFFgFfR9pOFRQkd2H26drojAdV0Yngs9\nRw+urVBMtOoujM6jdxDfN7s3bpR9KhLJY0iBWwEIIUs1mlVtilmZ/tuEp9bX4es6DjLke96fTLAx\nGkLX8nuIJUMf2AIikbba4OMR+Dmi3eMcGmdQTXOpKV9ZMAtqMgOAh1vbAJDJ29cpeGTvdBbHPDXM\n6grcRwkd2QWuSQgIcO19uGPfR8PLd/yyrWmYZNyFKoOd0fy+maP4l0jScPX2Q3JA13UYhpHLsVVV\nhb3A0IAbIxdTZbHXTkcj1J57fqHXFsH+2MXTlpnr+SQCrl6vn+kdrNfrMN74Bg48F29/9tlU7/GA\nAV1vluvP8Z9bFv63UQ89Vcc7rYsfZuFT34UJj6diqWcM9SBwYLIhlEYj1Tkvem2exHYCuJwXcu0N\nb3XR/uZbaGo2jJTVobdBwf+z3y/s2oz29wEAN29uol7hyKTp+ib0mSPkc7H2x2CGCbtRE3BmMWmu\nzTxxQNAIvFzP6cZaEw7XYVkWFEH9FZfdN0XwgHF0/HzvmxJJGqTABeD7Pnw/nxnatm1jPL48dsrm\nDA9m7kKvDQ8P4NdqC722CN5yfbxTIbmej6Io0HUd0+n0XBvCrdEA34w8vDvleewoGr4v8nP/XL/P\nUPG3R0M8FV1sVeBUAQjB+P59qGdseQ8cD3oUgptmqnNe9No8CQlDTKOokGvvm56PrWEf49ED+Bvp\nBG47iDCMGHrDYW4WmpPX5uFgAGqsIQzCyvx+ngVvNjHb2wUEnGONAIfTKcZcXBd9mmszTwZBgAZo\nrudkUArGFew9eICmILG4yH0zK/epindEQWnfV17FKcnVR1oUKsL6guN6ueeBz5zKZOB6jOMwQ6e7\nSDqBh12ebqueMYYH9hq6tfxvlndMHa+6AcJLKipEUUDWWmDDs7eSXR77b0mtuEqhSclxc1ve7IYM\nW4ELttdLfYxbKoUCLJU/nIWpH6DmuaAl+tEXIU7ouHhK3qLUKcH0mlsUJiBo8Hy93HYj/j0eC4g7\nLIoi75sSybJU+y68QrQVisEC3sbEN0fb1fDgJhFMnSoIXJVi10yXJtAfDuEaJp4q4HN9r6Eh4Bxv\neJdXvGirfW5mqcc5jMAvLCIMKLbJrBdG6CoU0d5u6mMohOCWSoVEyC3CNAxh+V4h75UF2r44oWMZ\nLEKufZPZhCqwc7a5W2YNNIownF2dcb2D0Qgzs4bt9pMWKomkbKTArQhtStCP2KXNR2xwBKhaYcH+\nl9ELI2wqFEYFJjdtN+o4sFsIguXHXe4cHEILfGyu518ZNynBe0xtodG9Fw178DiHHviFXgsGIYUM\nThhHDGPGsd2oI9pNX8EFim00cyIGK6z+uNV44SRmmtkqDHsYazrsnId3KIqCuutg7FV/gZRw/+AQ\nWhDg5sZG2acikTyBFLgVoa1QMACjS8QD6/dB2+3CuuYvoxdGqXNKRbO9uQmmKNg72F/63+44M9wa\nDYQ1d1zGXVPD17wA/iXC4CKB63IOw3NBauKaey7DLGjQQy+MoADY2twAH4/AMmS2dosUuIyjFlV/\notNxQkeY/VwtugIVXN2EXUCEV8PzMA6qf/0k9BwHN8fF3TclkmWohjKRoDWvDlyWhVu9iLBq+G8B\noGnbaEwn2BmMlv63uxzY8vONATrJuwwNCoDXL8nEjbeSz6vgAoY7Ay26gluIwGW4pVJoN24BlGaq\n4nZVBbthtFA0W1YcAFbOXk0R0PY6wDnYaJj5WHVCML3GAjeKIji1GpoFNDM1Qh/jJca2l00vKva+\nKZEsgxS4FUEjBPbcpnARVRO4uxXIwD1JZzpCz1s+EWNXr6Gb8xbkSTRC8D5Tv9SmcNFWssc49Nms\nUA9u0mSWV+RQQnJdEVUFvXkrU6NZV6Xw+OKDVLLgUArrCmzXE7sJEAIuwKZw3Su4zswBowrsAnZK\nGizC+Ap9lHuGiY5UEZKKIi/NCrGuUBxdMuyB9/sgFRG4iU+ySgJ3i4XYJcudTxiG2G+20K0Xt9UP\nAHdMDf/ohXAu+M5Jax1wZ+Duk40nLufQZk7BHlyAA8jbZRpbX+LvUdnqZmo0aysUBkEhjWYOVY7H\n11aZ44QOAY1mFqXX2oM7msSpBs1G/gtJGxwTWp376UVEUYSHzRa268UtsCWSZZACt0K0KcXgsgru\nsB9vL1aAxCd5syIeXADoair2rOXmxT88PESoqnhqM58RvefxDl1FjRK8doFNIUnLOCvSyWMMxmxa\ncIoCmb93foKGcf7YzoDS6WayKFBCCms0m6ka6kr1BS5wsb97Ga57isJoNgONIli1AgQuIZgoVyOe\nPr5vanhKNphJKkp1lIkEbYXiKDr/QcEZq5RFIfFJKhWqWG03m+jba5gtEbVzvz+ANXOEhasvikII\n3m9qeOkCgUtqFqDpZ0aFeRGD4XvxawrCnH/XeTaaHUUMHo+tBQCgbHUQ7e1mskUU1WjmaDos9WoI\nFFECt04JZpwX4nEug7Hno+7OCmmkaqgqJvrVyJS93++j5s6w1myWfSoSyZlIgVsh2srFHlw+nQBh\nWBmBWzX/LQB0b2yCcIad/cWTFHquh63JsJRw/rumjjf9EKNzvndCSNxodsawB5cz6H6xObiJwPVy\n1DK7IYNBYssOANCtLuC5mfyiSaNZ3sx0E/WrJHAFeXA5gNl1FbhBgEZBjVRNXcPELNYqlZbdmYet\ncTn3TYlkEeSVWSHaCr1Q4B4PeaiIwD3pk6wKpmmiPR5hZ7R4rNQuKLZKyi59VlPQpgQvX2RTOEeI\neByFV3A1krx3fmImua6SKDzaXgd0HVGGRrOOSvEgZIhyPG8/8OHrOqwrMjr0vIXTstTn39N1tSmM\nIoZGkM8o99M0ayZ83YB3BbJwe6DYCov5XCSSNEiBWyHWFYoJ5+dmo7JBH6TeANH1gs/sjHM55ZOs\nEp3ZBL0lsiT3anV0tXJ+DkII7tR0vHRBmsJ5W8kuCHTEDUNFQQmBQfK1KPROXVeE0nmjWYYkBU1B\nBOBhjo1m06kDAKibV0TgCqrg1uZDXq5rVNiEx+kGRdCsz8f1Tqo/rnevVq/k/V8iSZACt0K06cVZ\nuKzfB21VYyTiaZ9kldjiDLvqYqHsnufhqLmG7YL9tye5Y+r4VhDh4JwtdNpaf8KDG3KOiBDUStge\nzHua2WmBCzzy4abFphQ2JdjJ0aYwmfu+G9bV2GK+KKFjGTRCoBNc2ySFCaWwC/rZ7LnAHTlOIe+X\nFs/3cGivYbu5XEOvRFIk1VMnK0yDEqg4X+DywVH8UKoAp32SVWLbNPCgsVjjQ29/H5xQbN8sNkHh\nJNsqxZZCce8cmwJpt8GHj6coJBYBo4TPP89hDyHneBCyJxZOWZMUgPx9uI4bbysX0W0vgosSOpbl\nOicpjBUNjYJGkeu6DsNzMcq46Mib3f0DcErRvVHefVMiuYzqqZMVhhKC1gU+XDYYHD+Uyua0T7JK\nbLfWMK2iZW8cAAAgAElEQVTVMVhgStP94RityahUUZLYFM4b+pBYFPiJvNykycvUim9oisf15nPs\nhyEDA9A5ZRmhW12whw/Ao/QCtaPSXJMUpr4P03OvzNjSixI6lqVOr+80s4luwC6wcbDhzjDyy+kJ\nWJSd4QhrkxEaBWZwSyTLIgVuxWgrFP1zHhSsfwS6Vh2BW1X/1a3NTShhiJ2Dyx/cu0GArem4gLO6\nmLumhl7IsBs8KcBoqw1EEfjk0XkmFgFjQSuGSAySX5NZL4xgUwL7lPVC2eoCUQi2/zD1seOosPw8\nuJMgQM2vfnNQwnFCh4gs3Gs87GFq1tDUi/s9awQ+JgUkfmSh5wXYchZv5JVIykAK3IrRvmBcLxv0\nK1XBrarAVVUVN8YD7CzQqLFLVXR4+Q+TG6qCpzXlzGazxHd9UogkTV6mUXzDYTyuNz+Be9Z1RW0b\npN7I5MPtqgoOI5abf9gJI1gFdduLIt4dEDDN7JpaFDzfg6cbsGtmYe/ZCAOMK75Y2FVUdKLFG3kl\nkjKQArdirJ9jUeBhAD4eVSIi7DyfZJXoeDPsXjIVDgD26ja6JYjEs7hjarjnBk8MNCCaDtKwH+t4\ndzmHGkVQS7BW5OnBvWjhpHSyJSkkkXZ5+XCnjMEqKW4uLbS9LsSDG1sU8h+FXDTj8XxMb4FZ0zZn\nGKN61q+T7Fk2ukbxu0cSyTJUV6GsKOd5cNkw9pNWQeCe55OsEh0C7OoXV13G0wnGdRvba9WYxPMB\nU8dhxPDtM20KrccqbR7nMIJihzwk5JmisHvBwoludcEyCFyDEmwq+flwHc5RKyhOShR0rS3Eg2tR\nci0tCkmaQbNRXFpAgwATWt1hIZPpFKOGje7aWtmnIpFciBS4FWNdoTiK2BNVPNY/AigFaZZ/UznP\nJ1klulYND+0WoguaknoPD0BZhK2KdAK3FIq36+qZaQq0tQ5+otLmcUAP/EKHPCTk1WTmMo7DiJ07\nPEREkkKejWYOCKwrJvJIuw0uwoN7TS0KI9eF4bnQC8wetxWKiVbd6ujO/j4oi2SCgqTyVFehrCht\nhSIAMD31oOTDAchaC6QCorLK/tuEpzbW4es6Di4Isr8/mWBzNICuVcOiACQ2BR/stE2h/bhX0mUc\nuu+BlNDFnFeT2d5ceJ4rcLe6YEeH4BmmPMVRYflspTtUgVXtneUnoK022HDwWEJHGix6PQXu2PfR\n8IoZ05tgaxomRnGe32XpjafYGA0LFf0SSRrKV0uSxzhv2APrH1XCngBcDYG73mrB8Fz0js4XuLsh\nw1bF8iafNzVMGMe/+o83cMRbySc8uIzBcGegZVgUaD4e3F4YYVOhMM7JHFVubQGcI3qwl/o9OqqS\nXwVXUWEVlJcqirMSOtJQp+SJRfl1YBxGaBScjLFmGJia1oW7T2XSCyNsudUeRCGRAFLgVg6TEtQI\nQT86ZVEY9CsjcC/ySVYFSim2xkPszM4XsLuagS6p1kO5QSneaah46ZRNgbbbYMNHAtcLQxi+V4oH\nN7Yo5CNwL7quiGmCrm9kajTbVhWMGcd4gQbEZXE0HfUrkoGbcJzQkXFkb2JROG2tuuqMOUej4MZB\n26qBU4ppRaeZ7ao6Orhe37PkelJtlbKirCtPRoVVJSLsMp9kldgKPPT42RU1xhge2Gvo1IyCz+py\n7po6XnUDhCfEAm2tg4/H4EH8sHWDALrvl2RRyKfJrBdefl3RrU6mRrObKoUC5FLFnekG6hX2Tp7F\ncUJHRh9unRJEAPxrpnsmIGjwYtMh7Eb8Oz2eXh5zWDSMMezZa+hW8L4pkZxGCtwK0lIo+uy0RaEP\nWoExvZf5JKtEV6XYM8+ucPaHQ7iGiacqsGg4zXsNDQHneMN7ZFM4rrTNR/a6YVhak1kcEyb+uLsL\nWF+yNpophOCWSoX7cKMogmtcPYELPJnQkQZrbq26bkkKE6rALth1Ypk10CjC8ILdp7IYjEZwzRq2\n262yT0UiuRQpcCvIOo2TFBI452CDo2ORUyaX+SSrRLdRx4HdQhA8ucV4/+AQWuBjc738RcNpDErw\nnKk9NvSB2E1AUY4rbV4UwQgCoARBZZI4h1fkdvQ4YhgzfrnA3epmGvYA5OPDnU6n4ITCMq9eZYu2\n1jNXcK35yO7rNq53rOmwlWIfk4qioO46GGdopsyL+L4Z4ObGRtmnIpFcihS4FaR9KguXuzPA8ypR\nwb3MJ1kltjc3wRQFewf7T/y/njPDrdEASkU9k3dMDa97wbEVgFAKutYCn2eWeozDAAMhxS80DErA\nAIicY9QLIyiILQQXoWx1wccjsGn6MaHdHATuaBKfT6NWE3rcIhARFWaQ+GHiXLNhDxPdhF3CIrLh\neRgH1ZsU1nMc3BxX974pkZzkaiiVFeMJgTt/+JAKbKcv4pOsCk3bRmM6wf3B6In/1+PAll9s/M8y\nvMvQoAB43XtUfSat9nGlzeUcZknFMnMuqkU2mvVChlsqhXKJYKc3bgKUZrIpxFFh0RNRbFkYTeOG\nIMu6egL3dEJHGggh1y5JIYoiOLUamkbxVflG6OfSCJmVXlTt+6ZEchIpcCtIW6EYMo5o/rBg/T5g\nGCBm+Q/PqxARdpLOdIRdz3/i7/f0GroFbz0ug0YInjd13DthU6AnBK4HQC/JJmLMRajIRrNFryui\nqqA3b2VqNOuqFB7HYzagrIxdF1oQwNCvoEWh3c5sUQCu37CHqeOAUQV2CVX5BoswruBHuWeY6FT3\ntimRPIa8VCvIukLBAQznD4skIqyM7eiTjCOGyQI+ySqxxUL0yOPnG4Yh9pstdOvFN2gtwx1Twz96\n4fG270kh4oHCLFvgCnwA74bRwjsDWX24bYXCIBDaaDbxPJhXtLJFW+vgk0cJHWm5bsMekhSDpl18\nUokNjgmt1n02iiI8bLaxXfH7pkSSIAVuBVmjBASPhj3EArca/ttFfJJVoqupeGA9Pkf+4eEhQlXF\nU5vVbpR4hx4PDnh1nolLW+vHW8kepTBLmmpnzHW1KIsC43ypnYGsSQqUEOGNZhM/gBU8uVNwFUji\nB5OEjrRYlFyrFIXRbAYaRbBK2DmzCcFEUQt/34t4cDC/b8oGM8kV4eoolRVCIQRrlBxvocYCtwoJ\nCov5JKvEdrOJvr2G2YnInfv9AayZg6Ztl3hml0MJwQdMDfeOBW48rpdzDk9RYKjlRFJRQqALHNd7\nFDH4HNjWFq3gdhDt7WZKcRDdaDYJQ1gFDwQQBWnYcUJHP1tUWJ2Qa5WiMPZ81N1ZKQ1VDVXFpGJ2\nl51BHzXXwVqzWfapSCQLIQVuRTnZaMb6R6DtalRwr5I9AQC2b94A4Qw7+4+SFHquh63JELSkCugy\n3DF1vOmHGEUsnmTn+wgdB6GiwtTKq/DEWbhixEwvZDAJ0F7QckG3uoDngmdojBIucBmDVdHRqpeR\nJHRkjgq7ZhaFURCgUZLtZE3XMKlAz8VJejMPW+PRlbhvSiSAFLiVpX1i2AMfDCoxpneRIP6qYRgG\n1scj7IwexUrtgqJzRaptz2oK1hWKl93geCvZnY/sNXW9tPMyCYErSMwk/ttFPea0vQ4YRqaRvV2V\n4kHIHpsWl4Up47AKnnglEtLKHhVWp/RaWRTGEUOjJNuJXTPh6wa8CmXhxvfNq2nDkawmUuBWlDaN\nK7icMbBhH6RkgZv4JK9KRNhJtmYT9E5kSu7V6ugsuB1eNmRuU3jJ9eMUDdPEbBjHnplGeQJX5DSz\nZa8rQimUW51MArejKWAAHgpqNHMIhYWrK+5OJnSkpXbNUhQmPE4zKINmfT6ud1Kdcb17tfqVvP9L\nVhcpcCtKYlHg4xHAWOkV3GV9klWiwxl2535Vz/Nw1FzDdsX9tye5a+r4VhDhIIxAW23MxmMAgGma\npZ2TIdCDm8b6onSyJSnYlMKmRJhNYUoprCsw3e88aLud3YNLCabXaNDDhFLYJVWk7bnAHTlOKe9/\nGs/3cGivYbvZuPzFEklFkAK3oqwrFP2IH1dVym4yW9YnWSW6poEHjbgxore/D04otm9ulnxWi9NV\nKbZUintuANpax2w0BACYJU7NMikRkqIQco4HIVt6Op6y1cmUpACI9eE6iob6FZ7uFI/rlSkKJxkr\nGuyS7ne6rsP0XIzc2eUvLoDewwNwStG9cXXumxKJFLgVpa3EDwtnMACxbZCSOuYTlvVJVontVgvT\nWh2D0RD3h2O0xkNYtauT5UgIwd350AfaamHW70MJQ2hW8fmcCaKazB6GDAxY2jJCt7pgDx+AZ2js\nSiaaicDRddTVasU6LcPJhI601Oe2FVG+5rKZ6AYaJX6ndXeGkV+NXoHeaIS1yQiNEu85EsmySIFb\nUdrzKVtH02llMnCvqv/q1uYGlDDE/f1D7PoBtpzJ5f+oYtwxNfRChodb23DHIxi+B2KVJ9JNQoRM\nMuuFEWxKYC/Zma1sdYEoBNt/mPq9OypFT4AHlzEGRzdR18tdhGYhSejgs/Rb4olF47r4cKdmDc0S\nv9NG4GMiMOkjCz3vat43JauNFLgVpU4INAB9zy/dfwtczYiwBFVVcXM8QG/qYFdR0eHVeGgsww1V\nwdOagldvbsMFgR74IEaZHlwCV4COSXtdUdsGadgZkxQUHEYscxpEEASIVBWNEr+PrCQJHVmSFI4F\n7jWo4Hq+B083YNfK+04bYYBxRRYLu4qKThRe/kKJpEJcO4E7mUzw2c9+Fj/90z+NF154AX/2Z39W\n9imlghBy3GhWtsBN65OsElveDLuMY6/eRLfE9IEs3DU1vNJowdMNGGEAUmIepagmsywLp2TgQ1qS\nHYm9jFWyZKSrVWLTX1aShA6WIVvYItengjsex9/pWr28LXmbM4wrYgnbs2x0jau7QyFZTa6uYjmH\nF198EUEQ4Itf/CI+/elP48tf/jLu3btX9mmloq1Q9EFA2uUK3AcpfZJVokOAf7NsjOsNbK9dzUk8\n7zd1HFGKb9z+bhglV1NENZntZlg40Ywjew1KsKnQzI1m0/mUvEaJlhERJD7c1P+eENQIrsU0syS9\nwC5R4DYIMKHl+7on0ylGDRvdtbWyT0UiWYprJXBd18VXv/pVfPSjH4VlWXjmmWfwwQ9+EF/5ylfK\nPrVUrCsUA00vvYK7m9InWSW6Vg1HzRYoi7B1RTuBWwrF2zUV//S274NechyTiCYzl3EcRiy1t1vZ\n6oJlsCgAiQ83o8D1PFAWoVbidrYIaGs9UwUXAKxrMuxh5LowPBd6icNUbIViopVfNd3Z3wdlkUxQ\nkFw5rq5iOYOdnR1wzvH0008f/92zzz6Lt956q8SzSk8LHINavfQms6vsv014aiP+DDdHA+ja1bQo\nAMDdmg6mKDBKnpplCGgySxIMMgnco0PwDNOe4qiwbJ/lxPdhet6VH2FK222wYUaBSwica5CFO/J9\nNLxyxvQm2JqGSQV83TvjKTZGw1LFvkSShvL3PwTiui6sU9uE9Xods1k5WYLc9xH2j8Cm6abRrA0n\n6K+1MWq2QKPyHhrfCa6+wF1vtWB85yG2KpIrmZbnTQ1/0p/ALLlIZhKCGQcGGa7LbwQhNhUKI2XW\nqHJrC+Ac4Tf+FUp3O9UxtkLgqwFwlCED9sD1YBlX/1ZKW20EX/8O2DD9Z2Ex4NCJcBRkE4eu58Ep\n6b4NAAdBiAYvd0zummFgalg47PdTxzNSQhFEEUbDEVjKRfFbYYStqBoDJySSZbj6d+UTmKb5hJh1\nHAe1U4H4BwcHODg4OP4zpRQ3btwQfj7eG1/H7pf+19T/3u7exuBn/lv8e5cD7kjgmS3Pf1Y3oZQY\nZJ+8d9pzUBQFt4d9PKOkP0YVaCoK3tV/iJaAn4MQkvoYLY1hxjk+tZ/turxT09P/HPU6lFtbmP4v\n/3Pq92+3NjD5bz6Bf59Fj914Cm8/2LvS1xUAqLe24P4f/ztG/+FTqY9R/9BP4D89dwf/KWtQSdkL\n0a3beH7vO6V+pzdaLXAP+LVMOpsBs2wDPNC5jX/38P6Vv74lqwfhWZK9K4bruviZn/kZ/M7v/A5u\n374NAPjDP/xD3L9/H5/85CePX/fiiy/iC1/4wvGfX3jhBXzsYx8Tfj7McxGNsgmAoW6AlTyUgALY\n0NQrOeThJL7nQ1GVK3+jDoIAhBCoJQ8WOArCzKH+LVWBnmFrn80cRJNs+ZzDiMHPdASgtda8UsND\nzoJzjvDoEMhgMQg4x+AaNJkBwHqrDdM0Sj2Hw6MjeH7WqzM7t27cuPL3Tcnqca0ELgD89m//NlzX\nxSc+8Qns7+/jV3/1V/Hxj38cd+7cOX5NURVcILZITFNaFCSPUBQFzeb/3979x0Rd/3EAf56IHELi\nycmPaaAsRU0TmCTCLIY/2rDEpcJpm2KgaDjILNfc2KBa68dcZmQqhuLs1FqaMwu1sh8yWEyKlJg/\n7wAFhjdIQQ6Ou3t9/3B96lK/KpyCH56PzQ3f78/nzetuL/G5D5/P+4bg2rVrcPTg06voH+xN92Bv\nuh970z36Q2/qenmXIeq7VHWLAgBkZGQgPz8fqamp8Pb2xvz5813CLQDo9Xro9f88EWqxWO7bP34R\nUe0Plt7gcDj4froJe9O92Jvuw950L/Ym9UeqC7i+vr4utyMQERERUf/ycO9rQ0RERET0Hwy4RERE\nRKQqDLhEREREpCoMuERERESkKgy4RERERKQqDLhEREREpCoMuERERESkKgy4RERERKQqDLhERERE\npCoMuERERESkKgy4RERERKQqDLhEREREpCoaEZHeLoLoTiwWC7788kvMnz8fer2+t8shUrA3qa9i\nb1J/xiu49FCwWCwoKCiAxWLp7VKIXLA3qa9ib1J/xoBLRERERKrCgEtEREREquKRm5ub29tFEN0N\nb29vTJkyBYMHD+7tUohcsDepr2JvUn/Fh8yIiIiISFV4iwIRERERqQoDLhERERGpysDeLoD6n6+/\n/ho//PADzGYzpk2bhtdee02ZO3nyJIqKitDY2Ijg4GCsWrUK48aNU+YPHDiAw4cPo7W1FRMnTsTq\n1auh0+mU+d27d6O4uBh2ux1xcXFYuXIlPD09H+jro4dXV1cXtmzZgsrKSrS2tkKv1yM5ORlPP/00\nAKCmpgYfffQRzGYzAgMDsWLFCkyePFk5v6SkBEVFRWhubsa4ceOQlZWFgIAAZZ79Sd3Vk95sbm7G\n5s2bcf78eeXrkSNHuqzP3iTVEaIHrKSkREpLS+WTTz6R9957Txmvr6+X5ORkqaioELvdLkeOHJHF\nixdLa2uriIgcP35c0tPTpb6+Xjo7O+XDDz+U9evXK+cfOXJE0tPTpaGhQa5evSrr1q2THTt2POiX\nRw8xq9Uqu3fvloaGBnE6nVJVVSUpKSlSXV0tXV1dkpaWJvv27RObzSY///yzpKSkSEtLi4iI1NbW\nysKFC6WiokI6OjqkoKBA1q5dq6zN/qSe6ElvNjc3y+HDh+XMmTPy3HPPSV1dncva7E1SI96iQA9c\nbGwsYmJiMGTIEJfxiooKhIeHIzIyEh4eHpg9eza8vb1RVlYGACgrK8OsWbMQHByMQYMGwWAw4NSp\nU2hsbAQAfPfdd0hKSkJQUBCGDBkCg8GA77///oG/Pnp4abVavPDCCwgKCoJGo8GECRMwfvx4VFdX\n49SpU+js7MSCBQvg6emJ6dOnIyQkBCUlJQCAH3/8EVFRUYiMjISXlxcWL14Mk8mE2tpaAOxP6pme\n9KZOp0NiYiLGjh17y7XZm6RGDLjUZ4gI5BabepjN5tse/+/52tpahIWFKfNhYWG4evUqWlpa3F4r\n9Q8dHR04f/48QkNDUVtbi1GjRmHAgH9+bIaFhaGmpgbAjV8Rjx49WpkbPHgwgoKClHn2J7nTvfTm\nnbA3SY0YcKnPiIiIQHV1NU6ePAm73Y7i4mJcuXIFnZ2dAIApU6bg6NGjuHz5Mjo7O7Fnzx5oNBpl\nvqOjAz4+Psp6f39ttVof/Iuhh57T6cTGjRsxZswYREZGwmq1uvQXcKPH/u6v//bfnebZn9Rd99qb\nd8LeJDXiQ2bUZ4wcORKvvvoqioqKYLFYEB0djcmTJ8Pf3x8AMGPGDFgsFuTm5qKzsxPz5s2DVquF\nXq8HcONXeNevX1fWa29vB3Bjo3OieyEi2Lx5M5qbm5GXlweNRgNvb2+X/gKA69evK/2l1WqVnvtb\ne3u7yzz7k3qqO715J+xNUiNewaU+JSYmBps2bYLRaERWVhYuXbqE8PBwAIBGo4HBYEBBQQF27dqF\nJ598EiKC0NBQAEBISAhMJpOy1sWLF+Hn5+eyywLRnYgItmzZApPJhNzcXOU/+ZCQENTU1MDpdCrH\nmkwmpf9CQ0Nx8eJFZc5qtaKxsZH9SW7T3d68E/YmqREDLj1wDocDNpsNTqcTTqcTNpsNdrsdAHDu\n3Dk4HA60tbWhoKAAAQEBiIyMBAC0tbWhvr4eIoLGxkbk5+cjKSkJvr6+AG5c4T148CAaGxvR2tqK\nvXv3YsaMGb32OunhtHXrVpw5cwZ5eXkuH286adIkDBo0CPv370dXVxdOnDiBmpoaxMXFAQDi4+NR\nUVGB33//HTabDUajEaNGjUJISAgA9if1XHd7EwBsNhtsNhuAG1uO2Ww25TkG9iapET+qlx44o9GI\nvXv3uowlJCTg5Zdfxvr163HhwgUMGDAA0dHRSE9PV3ZbaGhowBtvvIErV67A19cXs2fPhsFgUB6s\nEBF89tln+Pbbb+FwOBAbG4tVq1ZxL0e6a01NTUhPT4enpyc8PDyU8QULFiA5ORlmsxn5+fkwm80I\nCAhARkaGyz64J06cQFFREVpaWhAeHo7s7GxlH1z2J/VET3tz7ty5N61ZUFCAwMBA9iapEgMuERER\nEakKb1EgIiIiIlVhwCUiIiIiVWHAJSIiIiJVYcAlIiIiIlVhwCUiIiIiVWHAJSIiIiJVYcAlIiIi\nIlVhwCUiIiIiVWHAJaJu+eqrr7B582a3r/vXX38hNzcXf/75p9vXJiKi/oEBl4i65X4G3Ly8PAZc\nIiLqNgZcIiI3EBF0dnb2dhlERAQGXCLqhtTUVBQVFaGqqgoajQYajQapqakAgNLSUiQkJMDHxwd+\nfn5YvHgxmpqaXM5/55138Nhjj0Gr1WL48OGYOXMmTCYTzGYzRo8eDQBYuHChsrbZbP6/9Zw6dQoa\njQbHjh1zGXc4HBgxYgTWrVunjFVXVyMpKQl+fn7w8fHBnDlzcOHCBZfzNmzYgOjoaPj5+SEgIADP\nPvsszp49e9N7MHHiRHzzzTeYPHkyvLy8cOjQoXt5G4mI6D5hwCWie5aTk4PExESEhYWhtLQUpaWl\nyMnJQWlpKeLj4+Hn54d9+/Zh27ZtKC8vR1JSknLurl27kJOTg7S0NBQXF2P79u2IiIjAtWvXEBwc\njP379wMA3n77bWXt4ODg/1vPpEmTMHXqVBQWFrqMFxcXo76+Hi+++CIA4OLFi4iNjUVzczN27twJ\no9GIK1euYMaMGS5XXy9duoTVq1fj4MGD2L59O5xOp3Lev9XX1yMrKwtr1qxBcXExIiIievS+EhGR\nmwgRUTcsXbpUHn/8cZexp556SmJjY8XpdCpjVVVVotFo5PDhwyIikpmZKVFRUbdd12QyCQD54osv\n7qme7du3i1arlebmZmXs+eefl9jYWOXvS5YskbCwMLFarcpYU1OT+Pr6yscff3zLde12u7S3t4uv\nr69s3bpVGV+6dKkAkLKysnuqk4iI7j9ewSUit2hvb0dJSQkWLlwIh8MBu90Ou92OsWPH4tFHH0V5\neTkAICoqCr/99hteeeUVnDhxAl1dXW75/gaDAZ6enjAajQAAi8WCQ4cOIS0tTTnm6NGjmDt3LgYO\nHKjUp9PpEBkZqdQHAGVlZZg1axb8/f0xcOBADB48GG1tbTfdpuDv74+pU6e6pX4iInIfBlwicouW\nlhY4HA6sWbMGnp6eLn9qa2tRV1cH4Ma9qx988AGOHDmC6dOnY/jw4cjOzobVau3R9/fx8cGiRYvw\n6aefAgB2794NLy8vJCcnK8dYLBZs3Ljxpvp++eUXpb7a2lrMnj0bDocDW7duRUlJCcrLyxEQEICO\njg6X7xkYGNijmomI6P4Y2NsFEJE6DB06FBqNBuvXr8e8efNumtfr9QCAAQMGIDs7G9nZ2bh8+TL2\n7t2L119/HXq9Hjk5OT2qYfny5di2bRsqKyuxY8cOJCcnw9fXV5kfNmwY5syZg5deeummcx955BEA\nN+7bbWtrw/79+zF06FAAgN1uv+n+WwDQaDQ9qpeIiO4PBlwi6pZBgwa5XNH08fHBtGnTUF1djbfe\neuuu1hgxYgTWrl0Lo9GI6upqZV0AN10tvRtTpkxBREQEsrKy8Mcff9y0T+/MmTNx+vRpREZGwsPD\n45ZrWK1WaDQaeHp6KmOff/457Hb7PddDRES9gwGXiLpl/PjxKCwsxJ49ezBmzBjo9Xq8//77SEhI\nQEpKCgwGA3Q6HS5duoRjx45h2bJliI+PR0ZGBnQ6HWJiYqDT6VBSUoLKykrlqmpQUBCGDh2KPXv2\nYPTo0fDy8sITTzyhBN87Wb58OTIzMxEeHo64uDiXuby8PERHR+OZZ57BihUrEBgYiMbGRvz000+Y\nPn06Fi1ahISEBADAsmXLkJGRgaqqKmzYsEG5mktERA+B3n7KjYgeTlevXhWDwSD+/v4CQJYuXSoi\nIuXl5ZKYmCh+fn7i7e0tY8aMkZUrV0pdXZ2IiOzcuVPi4uJk2LBhotVqZcKECbJp0yaXtQ8cOCDj\nx48XLy8vASAmk+mu66qvrxcA8u67795y/uzZs5KcnCz+/v7i5eUlo0aNkiVLlsjp06eVY3bt2iVh\nYWGi1WolJiZGfv31VwkNDZXMzEzlmFvtIkFERH2DRkSklzM2EZHbFBYWIiMjA3V1dQgKCurtcoiI\nqBfwFgUiUgWz2Yxz587hzTffREpKCsMtEVE/xiu4RNTnOZ1OOJ3O2857eHhg2bJlMBqNiI2Nxb59\n+3iwzaMAAACHSURBVLiFFxFRP8aAS0R9XmpqKoqKim47f/z4ccTHxz+4goiIqE9jwCWiPs9sNsNi\nsdx2Pjw8XNnHloiIiAGXiIiIiFSFH9VLRERERKrCgEtEREREqsKAS0RERESqwoBLRERERKrCgEtE\nREREqsKAS0RERESqwoBLRERERKrCgEtEREREqvI/NSO8kx2SKZMAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11be37e80>"
+       "<matplotlib.figure.Figure at 0x11c7550f0>"
       ]
      },
      "metadata": {},
@@ -5465,10 +5988,10 @@
     {
      "data": {
       "text/plain": [
-       "<ggplot: (-9223372036556999694)>"
+       "<ggplot: (-9223372036557208751)>"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5481,14 +6004,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
+   "execution_count": 95,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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k5MH/4xa94xEREcWkZ599FpmZmbDZbMjJycETTzyB22+/HV999RXi4uIQFxcH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\npOQptIqIiIhIyfv/ckSr7ib5D3cAAAAASUVORK5CYII=\n",
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8G8EruGjANRoVNKKAH3mFLCIiIooSDK1RTBBF6AcOhe/EcXi2fFvi50mCgOu1anYRICIi\noqjB0BrlRIsFuptugfPjNfBnnyvx81roZPzq9SObPVuJiIgoCjC0xgC5RWuoatSE491lJd4mUFUl\nIU0lcrWViIiIogJDawwQBAH6foPhP3kCnq0l2yYgCML5nq0uL3u2EhERUcRjaI0RYoIFul43w/nx\nGgRyS9ZNoJlWRp4/gCNebhEgIiKiyMbQGkPkFq2hqpYBx6oVJbrogFkSUV+jwjaHuxyqIyIiIio9\nhtYYIogidP2HwHfkV3h/2FGi57TSyfjR7YWbPVuJiIgogjG0xhgpKRnabj3hXLsKAav1X4+/RqOG\nDAG72LOViIiIIhhDawzStOsIMSERzrXv/+uxKkHA9Tr2bCUiIqLIxtAagwRJgq7/EHh3/wjvL/v/\n9fiWf/ZsPceerURERBShGFpjlKpqNWjadoRj9TsIuv/5RKuqahWqqiSuthIREVHEYmiNYdquPSBA\ngOvzj//12BY6GdudHvZsJSIioojE0BrDBI0Gur4D4f52A3wnfv/HY5vp1CgMBHHQ4yun6oiIiIhK\njqE1xqnr1of6usZwvr/iHy/xahRFNNCosY1bBIiIiCgCMbRWALrefRHIOQfP5k3/eFwrvYzdLi8c\n/xBuiYiIiJTA0FoBiGYztD36wPnpOgTy8y57XF1ZBYMoYKfTW47VEREREf07htYKQm7RGlLlKnCu\nuXzvVkkQ0EInc4sAERERRRyG1gpCEEXo+w6Cd/9eePfvvexxrXQyfvf5ccLLE7KIiIgocjC0ViBS\n5SrQtO8MxwfvXrZ3a7JKQi21hK1cbSUiIqIIolK6AK/Xi/nz52P37t2wWq1ISkrCgAED0L59ewDA\nsWPHMGfOHGRlZSE1NRXjx49Hw4YNFa46emkzu8P70y64Pv8YuptuveQxrfUavFfoRB9TELIglHOF\nRERERBdTfKXV7/fDYrHg2WefxcqVK3H33Xfjtddew4EDB+Dz+TB16lQ0b94cK1aswKBBgzB9+nTk\n5+crXXbUEmQZulsHwP3tBvhPnbjkMY20agBB7HbxhCwiIiKKDIqHVq1Wi9tuuw2VKlWCIAioX78+\n6tWrh19++QV79+6F2+1Gv379oFar0bZtW6Snp2Pz5s1Klx3V1PWugfrahnC8v/KSvVtlQUAznYwt\n3CJAREREEULx0Pp3LpcLv/76KzIyMnD8+HFUr14dovi/MmvUqIFjx44pWGFs0PXpC/8fZ+DZdukv\nAK11Mg57fDjr85dzZUREREQXU3xP64UCgQBefvll1K5dG40bN8ahQ4dgMBiKHWMwGHD27Nli92Vn\nZyM7O7votiiKSE5ODnl9As7v75QkKeRjlzfJkgh9zz5wfrIW2oaNIZrjij2eIUnIUDuxzeXFLXFy\n0f2CIMTE+y+tv957Rf4ZcA5wDnAOcA5U9DlAyoiY0BoMBjFv3jzk5ubi6aefhiAI0Ol0sNvtxY6z\n2+3Q6XTF7lu1ahUWLFhQdHvkyJGYMGFCyGuUJAkIAmazOeRjKyG+Vx+c+PF7eD9eg0p33XfR45lB\nEe/9kYvh6fFQif87IUuW5YuOrWhiZQ6UFucA5wDnAOcA5wCVt4gIrcFgEPPnz8fRo0cxderUolCa\nnp6OVatWIRAIFG0ROHr0KNq1a1fs+X379i3qNgCcX2nNy7v8lZ9Ky+/3A6KEwsLC8/8dA7R9B6Lg\npefwx5bvINe7pthj9YMBuAJ+bDzzB5roNADOr3T//YtERSJJEsxmc0zNgSvFOcA5wDnAORCuOZCQ\nkBDyMSl2RERoff3113Hw4EE8++yz0Ov1Rfc3aNAAsixj9erV6NOnD7Zv345jx46hTZs2xZ6flJSE\npKSkotvZ2dlh+SAJIgjgfHiNlQ8qoVIVaNp2gP29FRAfmQLhgm/OMoCmWhnf2lxoKJ+fKsFgMGbe\ne1nE0hy4UpwD53EOVMz3fiHOgYr53kk5iofWs2fP4pNPPoFarcbo0aOL7u/Xrx8GDBiAxx9/HHPn\nzsXKlSuRkpKCSZMmIT4+XsGKY4+2a094dv8I1+efQNfr5mKPtdHLeDHHhmyfH0kq7l8iIiIiZSge\nWlNSUrB27drLPl69enXMmDGjHCuqeASNBvpbB8C+eAHkJtdDqlK16LF0lYSqKgmbnR70Men+YRQi\nIiKi8Im4llekDHX9Bud7t763oljvVkEQcINexjanB75gUMEKiYiIqCJjaKUiupv7wX/uLDxbNhW7\nv5lWhjcYxE+8QhYREREphKGViojmOOh69YFz/UcI5OUW3a8Rz18h6zuHW8HqiIiIqCJjaKVi5Oat\nIVWpCsfqdxC8YDvADToNfvX6cdLN1VYiIiIqfwytVIwgitD3HwLf4YPw/vRD0f1pagk11RK+KrAp\nWB0RERFVVAytdBEpJRXazO5wfvg+Avb/hdS2eg02W+1wBXhCFhEREZUvhla6JE2HLhDi4uBcs6ro\nvkZaNTSCgJ0uj4KVERERUUXE0EqXJEgS9AOGwvvTD/D+vA8AoBIEdIgzYpPDXWy/KxEREVG4MbTS\nZamqVoOmQ2c4Vq1E0OkEAHSIM+APXwCHPT6FqyMiIqKKhKGV/pE2swcEjQbOj1YDACwqFRpp1djg\n4BYBIiIiKj8MrfSPBLUa+oFD4dm5Dd6DPwMA2us12Ov2IsfnV7g6IiIiqigYWulfqTKugqZ9Zzje\nXY6A04EaaglVVRI2cbWViIiIyglDK5WItltPCBot8t5ZBkEQ0MGgwRanB262vyIiIqJywNBKJSKo\n1dAPGgbH9i3w7t+LJlo1VAKwne2viIiIqBwwtFKJqdIzYO5+ExzvLYdkt6GdXoNv7G4E2P6KiIiI\nwoyhla6IuWdviHHxcL6/Am10auT5A9jvZvsrIiIiCi+GVroigqSCfsgIeA/8As3329FcJ+Mru0vp\nsoiIiCjGMbTSFZNSK0F3081wrnkf7d02/Or145iXq61EREQUPgytVCpy63ZQ1agN84q3cK0s4Su7\nW+mSiIiIKIYxtFKpCIIA/cChCOTnou3eH/Cjy4tsXmyAiIiIwoShlUpNNJmgHzQcVdatQrrPg68d\nXG0lIiKi8GBopTJRX10P2g5d0ObLddjq8MAaCChdEhEREcUghlYqM+2NvXCNLR/x1nxssLGTABER\nEYUeQyuVmSBJMA0dhXY7vsPGQgecvLQrERFFiQ8//BDz5s1TugwqAYZWCgkxPgGtrm8CjcOGDUeP\nKV0OERFRiTC0Rg+GVgoZ7dX10cmai29ENRznzildDhEREcWQUoXWiRMn4vDhw6GuhWJA20YNIQki\nNmzfjqCb3QSIiChyjRw5Em+99Rb2798PQRAgCAISExMhCMJFOScvLw86na5oVXbkyJG49tprsX79\nelx77bXQarVo2rQptm3bdtHrLF68GNdddx20Wi3S0tIwZcoU+P1sE3mlShValy5dirp166Jt27Z4\n66234HA4Ql0XRSlZkpAZb8Smaxoj/51lCLKbABERRaj/+7//Q48ePVCjRg1s3boVW7duxY4dO5CW\nloZFixYVO3b58uUAgCFDhhTdd/r0adx111145JFH8O6770Kj0aBbt244e/Zs0TEvvfQSxo4di27d\nuuGjjz7CY489htmzZ2PKlCnl8yZjSKlC6++//461a9ciNTUV48ePR+XKlTF+/Hhs3bo11PVRFLoh\nzghRp8cWnQmuzz9RuhwiIqJLqlmzJpKTk6HT6dCyZUu0bNkSNWvWxKhRo7BkyZJiq6GLFi3Crbfe\nivj4+KL7cnNzsXDhQowYMQK9e/fG+vXrEQwGMWvWLACA1WrFk08+iUcffRQvvvgiMjMzce+99+LF\nF1/E7NmzkZOTU+7vOZqVKrSKooiePXvi/fffx6lTp/D0009jx44duOGGG3DNNddg5syZxb5lUMUi\nCwIyTTp826YTrJu+gWfXTqVLIiIiKrExY8bg9OnT+PTTTwEAe/bswa5duzBmzJhix8XFxaFTp07F\nbnfp0gXbt28HAGzZsgU2mw39+/eHz+cr+telSxc4nU7s27ev/N5UDCjziViJiYm4//77sWTJErRt\n2xa//PILHnnkEVSrVg0jRozAOZ6QUyG10WsgqVT4YcgYON5ZBt/RI0qXREREVCLVq1dHZmYmFi5c\nCOD8KutVV12Fjh07FjsuOTn5ouempqbi9OnTAIDs7GwAQJMmTaBWq4v+1a5dG8D5v1xTyZUptBYU\nFOC1117D9ddfj8aNG6OwsBCvvvoqTp06hddeew3ffvstBg0aFKpaKYrIgoBuRi2+SU5D4Ib2sL/5\nOvxn/1C6LCIiohIZN24c1q1bh5MnT2LZsmUYNWoUBEEodsylFub++OMPVK5cGQBgsVgAAKtXr8bO\nnTsv+te9e/fwv5EYoirNk7766issWrQIH374IVQqFQYPHozXX38dTZs2LTpm9OjRqFatGm666aaQ\nFUvRpbVOxld2N7a274YOOdmwL5gH4z0PQjTHKV0aERERAECWZbhcF1/NsU+fPkhISMCQIUOQm5uL\nkSNHXnRMQUEBvv7666ItAgUFBfjyyy9x9913AwBatWoFvV6PEydO4JZbbgnr+6gISrXSmpmZid9+\n+w1z5szB6dOnMX/+/GKB9S916tTB4MGDy1wkRSeVIKC7UYuvnR74Bw+HEB8P24J5CDjZbYKIiCJD\nvXr1kJWVhRUrVuD7779HVlYWAECtVmPEiBHYtGkTMjMzUa1atYuea7FYMGbMGCxZsgRr165F9+7d\nEQwGcf/99wMA4uPj8cwzz+DRRx/FY489hvXr1+Pzzz/H/Pnz0b17d3ZfukKlCq179uzB1q1bMXr0\naOj1+ssel5GRgTfffLPUxVH0a65VwyKJ+Nzth2HU7QAA+8L57OFKREQRYcyYMejfvz/uueceNGvW\nDE899VTRY3+tjo4ePfqSz61cuTLmzp2L5557Dv3794fL5cJnn32G1NTUomMeeughvPnmm/jmm2/Q\nt29f9O/fH2+88QaaNWsGWZbD+t5iTalC67333osDBw5c8rFDhw4VO5OOKjZRENDbqMO3Dg9yZQ2M\n4+9G0GaFffEbCHo9SpdHREQVnNlsxooVK5CdnY1gMIjFixcXPbZ+/XokJiaiT58+l31+z5498fPP\nP8PtdmPXrl1o3br1RccMGjQIO3bsgMPhQEFBAXbt2oVnnnkGKlWpdmlWWKUKrRs2bEBhYeElHyss\nLMSmTZvKVBTFlms1KlyllrDW5oJoMsN4x70IZGfDvngBgl6v0uUREREVc/DgQaxduxZz5szBHXfc\nAY1Go3RJhDJ0D/j7GXR/2bJlC1JSUkpdEMUeQRBwi1mHH11eHPX4IMYnwHDnvfCf/QP2N1/niisR\nEUWU22+/HQMGDED79u0xadIkpcuhP5V4XXr69OmYPn06gPMhpGPHjhDF4pnX7XbD5/PhrrvuCm2V\nFPUy1Cpcr1VjldWJhyxGSJZEmO68H7b5r8D+39dgGHU7BK1W6TKJiIiwYcOGfz3mwm0EVD5KHFpb\nt26Nhx56CMFgEM888wwGDx6MqlWrFjtGlmXUq1ePba7oknqbdHjmXCG+d3nRTCdDtFhgvPsB2F6f\nA9vrc2AYeydEg1HpMomIqAJwuVxwF+QjeIl2V2VhrlwFIk+wCosSh9b27dujffv2AM6vtI4dOxZp\naWlhK4xiT4IkoqtRizVWJ67TqKERBYhx8TDe/QDsC+bBNvclGMbeBSkxSelSiYgoxgU8bmQ/PRnB\nELedEkaMRVzHLiEdk84r1WlrTz75ZKjroAqis0GDrQ43PrW70MekAwCIBiOMd94H+5KFsM2dCcOo\n26FKr65soUREFNu8XgQdDiQ8OBFSXHxIhvQX5CP4x+mQjEUXK3Fo7d27N2bOnInatWujd+/e/3is\nIAhYs2ZNmYuj2CMLAvqadViU70ALnYxKKgkAIGg0MIy+Hc4P3oVt3ivQDxoKudHFF6wgIiIKJSku\nHpIlMXQDMrSGTYlDq9Vqhd/vB3C+rdXlugcQ/ZvrNGrU1ajwXqETExIMRXNJkCTo+g6CmFIJjuVv\nwX/qBLQ33gRBLHWTCyIiIooRJQ6t33zzTdF/l+SsOqLLEQQB/U06/CfbWnRS1oWPadt1hFSpMhxv\nvwn/78ehHzICosmsYMVERETRqVKlSli5ciU6dOgQkvEmTpyIM2fOKNI9IaRLWB4P+21SySSpJNxo\n1GKV1QlbIHDR4+o6dWG6/zHqV0tfAAAgAElEQVQE3W5YX3oO3kO/KFAlERFR+CxevBgtW7ZUuoyo\nUarQunTpUsyZM6fo9r59+1C7dm3o9Xp06NABZ8+eDVmBFLu6GDQwiwI+KHRe8nHRYoHxrvshN20O\n+4J5cK5ZxQsREBERlQOfz6d0CRcpVWh98cUXi11Y4J577oEsy3j55Zdx+vRpTJ48OWQFUuySBAG3\nxemx0+XFz+5LX85VUKmg63UzDOMnwLP3J1hfeh6+rN/KuVIiIqKymTFjBqpVqwaTyYQaNWpg6tSp\nuOOOO7Bz504YjUYYjUbY7XZ8//33aN26NeLj41GpUiXcddddcLvdReMIgoA33ngDdevWRVxcHIYO\nHVrsL92zZs1CWloaUlJSMHPmzGI1lGTsefPmoW7duoiPP99RYc+ePWjWrBlMJhN69uyJvLy8MP+k\nLq9UoTUrKwv169cHAGRnZ+Pbb7/FzJkzMWHCBDzzzDP47LPPQlokxa4MtQqdDBosL3DAGQhe9jh1\n7athfmgyVDVqwvbqLDhWvYOAM7S99YiIiMLh4MGDeOKJJ/Dll1/CarVi8+bN6Nu3L+bPn49mzZrB\nZrPBZrPBYDBAkiTMmDED2dnZ2L59OzZu3Fjsr9sA8P7772Pz5s04fPgwtm7dirfffhsA8MUXX+DZ\nZ5/FRx99hOPHj+Pw4cPIzs4uel5Jxn733XexadMmnDt3Dl6vF3369MEtt9yC3Nxc3HfffVi6dGn4\nf2CXUarQKopiUar/5ptvoFar0bFjRwBA5cqVkZOTE7oKKeb1MGqhFQS8b730NoG/CDod9P2HwHD7\nPfAdOQTrc8/AvX0LgpfYE0tERBQpVCoVgsEg9u3bB6fTicqVKxct/v1d48aN0bp1a6hUKmRkZGD8\n+PHYuHFjsWMmT56MxMREpKSkoGfPnti1axcAYMWKFRg5ciSaNGkCrVaL5557DoELfkeWZOyJEyci\nJSUFOp0OW7duhd1ux8SJE6FWq9G1a1d069YtxD+dkitVaG3YsCHmzZuH/fv3Y/bs2ejUqRM0Gg0A\n4Pjx40hJSQlpkRTbZEHAsHg9djo92O369z2r6lp1YHpwEjQdusC5djWss56H98DPCAYvv1JLRESk\nlJo1a+Ktt97C7NmzkZqaip49e+LAgQOXPPbQoUPo1asXKlWqBLPZjEmTJhVbLQXOdwT4i16vh81m\nAwCcOnUK6enpRY/Fx8fDbP5f952SjJ2RkVH036dOnUJaWlqxLaEXPl7eShVap02bhk2bNuG6667D\n3r178fTTTxc99sEHH6B58+YhK5Aqhgy1CjcatVhe4ES+/99XTgWVCtqOXWCe+CRUV9WAfdF82F57\nBd5fDzG8EhFRxBkwYAA2btyIM2fOoGbNmhg3btwle97feeedqFWrFg4dOoTCwkJMnz69xL/XqlSp\nguPHjxfdzs/PR2Fh4RWNfWFNVapUwcmTJ4ut1l44fnkr1WVc27Rpg+PHj+PQoUOoWbNm0WZdABgz\nZgxq1aoVsgJLQ5blopXfUJL+/KZhMBgqbDBSqVQwmUxhGbuf0YhDJ85ihc2Nh9OSIZbkAhYmE+JG\njIWvx00oWLcG9tfnQK5RC+ZuPaFt0DDkFyb46//MnAPhmQPRgHOAc4BzgHPgSh08eBC///47brjh\nBmg0GhiNRkiShNTUVJw8eRJut7sot1itVpjNZphMJhw6dAjz589HXFxciV5n4MCBuO222zBs2DDU\nrVsXkydPLrZKeqVjt2rVCjqdDi+88AIeeughbNy4EZ9++ikGDBhQth9IKZUqtAKAyWRC06YXX2az\nR48eZSooFDweT1h6xvoDAUAUYbfbi64OVtGYTCZYrdawjT/MqMFzOVasPpONbkZtyZ+o1UPuNxhS\nhy5wf/MFst+YCzEhEZob2kFu2gKC9grG+geSJEGWZc6BMM6BSMc5wDnAORC+ORCOBadI4Ha7MWXK\nFPz888+QJAlNmjTB/PnzUaNGDTRq1AiVK1dGIBDAyZMnMWPGDIwfPx4vvfQSGjdujP79++Pzzz8v\n0et069YNkyZNQs+ePeH1evHYY48hKSmp6PErHVutVmPNmjUYO3Ysnn32WbRv3x7Dhg0r1nGgPAnB\nUn5NPHjwIFatWoUTJ07A5XIVH1QQsHDhwpAUWBp/358RKst+2gO3qMK4xg34QRVGP7o8eDPfgXst\nRtSSS/e9KmAthHvLt/Bs24ygxw258fWQm7eCVC2jTJcgliQJCQkJyMvL4xyooDgHOAc4B8I3By4M\nWOHmyMnGqYcmIOnp5yBZEkMypj83Bzj0C+Izu4dkPCquVIlg6dKlGDVqFLRaLTIyMiDLcrHHyxIK\niBprZRzR+7Ao346JiSaYpSv/E79oMkPXrSe0nbvBu28PPDu2wDZnJsTkVMhNm0HdqCmkxPL7cCQi\nIqKyKVVonTp1Kvr164dFixZBr9eHuiYi3GzS4ZjXj4X5dtxrMUIq5RchQaWC3KgJ5EZNEMjLhWfX\nTnh+2AnX+o8gVcuA+rpGUDdoBCkpOcTvgIiIiEKpVKH11KlTeO211xhYKWxUgoAx8Qa8kGPF+1Yn\nBprLPtfEBAu0nbtB06krAqdPwbPnR3h2boPr4zUQUytDfU0DqOtfCym9eshP4CIiIqKyKVVobdeu\nHfbt24fOnTuHuh6iIvGSiLHxBszOtaGKSkJbfWg26AuCAKlKGnRV0qC7sRf8Z8/Au38vvD/vg/ub\nLyBodVBdXRfqq+tDdXU9iOaSnbVJRERE4VOq0Dpt2jQMHToUWq0WmZmZxVpe/cVisZS5OKIasgpD\n4vR4u8CBJElEPY065K8hpVSClFIJ2o6ZCDjs8B38Bd4DP8P5yRoE33kbYuUqUNepC1WduhBrXR3y\n1yciIuX4C/JDOpYUstHo70oVWps0aQLgfJPay510VVHPqKTQa66TcdZ3fn/r/RYTqqrD95Eg6g3n\nOw00vh7BYBD+UyfhO/QLfIcOwL15E+wAnHXqQqhRG2LtqyFVrsKtBERE0UithmA0Ie/lF0M6bPLw\n0SEdj/6nVKF10aJF7BBA5aqnUYtcfwDz8mx4yGJEoir832UFQYAqrSpUaVWBjpkIej0IZB2FdOw3\nWHftgH/dBxBMJqjq1IP66nrntxIYjGGvi4iIyk5vjkPNuQuULoOuQKlC68iRI0NcBtE/EwQBQ+L0\neD3Pjrl5djxgMZaqFVaZalDLkOvWR0KrNpC69YI3Lw++gz/De+gXOD98D0GnE1LGVVDXuwbqa66D\nmFqJX+6IiIhCpNRXxAKAvLw87Nu3D7///ju6d++OhIQEuFwuyLJc7LJhRKGgEgSMjTdgbp4Nr+bZ\ncK/FCIOC80w0myE3awm5WUsEAwH4j2fB+8s+eH7aBdf6jyAmJUN9bcPzLbXSy3ZRAyIiCj1fMIhA\niC/FqxKEkl2GnK5YqUJrIBDA448/jtmzZ8PhcEAQBOzcuRMJCQm49dZb0aJFCzz55JOhrpUIGlHA\nnQkGzM6149VcOyZYDNBHwBckQRShql4Dquo1oOveG/7cHHj37YF3709wb/wKQlw85EZNoW58PaQq\naQywREQKK3A48EDWaTgCoQ2tY6sko4uFXWfCoVSh9YknnsDcuXMxc+ZMdO7cGXXq1Cl6rHfv3vjv\nf//L0EphoxdFTLAYMCfXhrkRFFwvJFkSIbXrCG27jggUFsC75yd4fvoB7g1fQkytDPn6FpCbNodo\nNitdKhFRheQNAo5AEBMrJSBeCs15Evl+P06HeOWW/qdUoXXx4sWYNm0abr/99ou6BNSsWRNHjhwJ\nSXFEl2MURdxjMWJurh2zc+24O8EAUznvcS0p0RwHzQ3tobmhPfw52fDu2gnPtu/gWr8WqnrXQtOy\nDVRX12MXAiIiBcRLUkhP7j0dCNlQ9DelCq05OTmoV6/eJR/z+/3wer1lKoqoJIyiiHstBszLs2NW\nrg0TLEZYIjS4/kVKTIKU2R2aLjfC/9uvcG/fAvviBRDj4yG3agu5RSuIOl5pjoiI6O9K9Ru+Tp06\n+OKLLy752IYNG3DttdeWqSiiktKLIu5JOB9WX8qx4qQ3OvoDC4IAVc3aMAwZAfMTz0Ju0Rru7zag\ncOrjcHz4Pvy5OUqXSEREFFFKtdL6wAMPYNy4cVCr1ejXrx8A4MSJE9i6dStmz56NxYsXh7JGon+k\nEQXckWDA0gIHXs61Ymy8AVeH4cpZ4SIajNB26gpN+87w7t0N98avYH1uE9SNmkLbqSukSpWVLpGI\niKjIyJEjUalSJTz33HPl+rql7tOam5uLp556CtOmTQMA3HzzzdDr9Xj22WcxYMCAkBZJ9G9UgoAR\ncXqss7nwap4dA806tNFrlC7rigiSBLlRE6gbNobvyGG4v/oM1pnToG7YGNrMHpBSKyldIhERlVL1\n6tUxf/583HjjjUqXErVK3af1wQcfxPjx47FlyxZkZ2fDYrGgVatWiItjmwdShigI6G3SIVkSsbLQ\niZM+P/qadJCirL2UIAhQ16oDda068GX9Btfnn8A64z9QN2kGbbeekCyJSpdIREQxyufzQaUqUxv/\nsCnxntYlS5Zc9G/16tU4c+YMfD4fzp49izVr1hQ9RqSUVnoN7rUY8aPLizm5NhT6o/dUTlX1GjCO\nnwDjHfchkJMN6/NT4Vy7GgGHQ+nSiIiohAYPHozjx4/jlltugdFoxJQpU3D48GF0794dSUlJqFmz\nJubNm1d0/FNPPYW+ffti9OjRMJvNqF27Nnbs2IElS5YgIyMDSUlJmDVrVrHjb731VgwdOhQmkwnX\nXHMNNm7cWPT4mTNn0LdvXyQlJeGqq67CtGnTEAic/924YcMGVKpUCS+//DLS0tLQq1evoporV66M\nuLg4tG3bFnv37i2nn9bllThK//3SrX81Rw9e0I/swobpw4cPL2NpRKVXU1bhsUQT/ptvx3M5VoyO\nN6CWHJnfHEtCVbMWjHc/AN/+vXB+vAae77dB27Un5FY3QAhRf0EiIgqPFStWYOvWrUXbAxwOB+rW\nrYtJkyZh7dq1+O2335CZmYnatWsjMzMTALBu3Tq89957WLBgASZOnIj+/fuje/fuOHDgAPbs2YO2\nbduiX79+qFatGgBg7dq1ePPNN7F48WIsW7YMffr0wdGjR5GQkIDBgwfjqquuwvHjx3Hq1CnceOON\nSE5Oxrhx4wAA2dnZOHr0KI4cOVKU67p27YrXX38dGo0GkydPxuDBg7Fv3z5lfoB/KvFKa15eXtG/\nnTt3IiMjA48//jh2796NM2fOYPfu3ZgyZQoyMjKwffv2cNZMVCLxkoj7LUY00aoxO9eGT22ukF+u\nrzwJggD1tdfB9PBkaDN7wPXZOlhnPQffkcNKl0ZERFdg3bp1qFy5Mu68806o1WpcffXVGDduHFas\nWFF0TKtWrdC7d29IklS0UvvUU09Bp9OhRYsWqF69Onbv3l10fMOGDTFs2DCoVCqMGDECV111FT7+\n+GOcOHECGzduxMyZM6HX61GrVi08/PDDxf4qHgwGMX36dGi1Wuh0OgDAqFGjYDabodFo8MQTT2D/\n/v3IyVG2s02Jl54u3Ks6ceJEjB8/HhMnTiy6LyUlBQ0aNIBOp8Njjz2Gr776KrSVEpWCShDQz6xH\nbVmFZQVOHPT4MDxOj4QI7+f6TwRJgqZtB6ibXA/X+o9gmz8b6ibNoOt1C0STSenyiIjoX2RlZeHH\nH39EfHx80X1+vx9t27Ytul2p0v9OvtXr9Ze8z2azFd1OT08v9hoZGRk4efIkTp48ibi4OCQkJBQ9\nVr16dZw8ebLodlJSUtFr/FXLlClT8N577+HcuXMQ/7z4TXZ2NhITlTuvolS/ubds2YKmTZte8rGm\nTZti27ZtZSqKKNQaamVMSjIhGAxiWrYV3zs9SpdUZqLBCH2/wTDe8xACZ07B+sJUuHdsLbZlh4iI\nIsOFWyjT09PRunVr5OfnF/2zWq345JNPSj3+8ePHL7qdlpaGtLQ0FBQUID8/v+ixrKwspKWlXbI2\nAFi+fDlWr16NL774AgUFBTh27BgAKP77pVShNSUlBe+8884lH1u5ciWSk5PLVBRROCRIIu61GNHV\noMHSAgcW5dthC0TvSVp/UaVXh/HeR6Dp3A3OD96F/Y258OdkK10WERFdIDU1tegy97169UJWVhYW\nLlwIt9sNn8+HvXv3YufOnaUef/fu3Vi+fDl8Ph+WLl2KI0eOoEePHqhatSratm2Lhx9+GE6nE0eO\nHMHMmTMxbNiwy45ltVqh0WiQmJgIp9OJxx9/vNR1hVKpQuvkyZOxaNEidOzYEa+88gpWrFiBV155\nBR06dMDixYsxZcqUUNdJFBKiICDTqMWjiSb84fPjP9lW/OSK/lVXQZKg7dAZpoenAMEgrDOnwb3l\nW8W/FRMR0XmTJk3CCy+8gPj4eDz//PP44osvsHbtWlSrVg3JyckYP348CgsLSz1+79698cknnyAh\nIQHTp0/HBx98AIvFAuD8iWA5OTmoWrUqOnXqhOHDh2Ps2LGXHWv48OGoUaMG0tLSUK9ePTRr1qzU\ndYWSECzlb7V169bhP//5D3744Yeinl5NmjTBlClTcNNNN4W6ziuSnR2eVaZlP+2BW1RhXOMG8Puj\n43KhoWYymWC1WpUuIyR8wSA+s7nwmd2Nhlo1+pt0MP/LXldJkpCQkIC8vLyInQPBYBCerd/Bue4D\nqKrXgH7gUIhx8f/+xBKKpTlQGtEwB8KNc4BzIFxzICkpKeRjXk623YEJR0/hubQkJKpC04Ulx+fH\nLwGge2LoPnNL4qmnnsKBAwewcuXKcn3d8lbqHkC9evVCr169EAgEcO7cOSQnJxdt1CWKBipBQE+T\nDo20MpYXOvBsthW3mLRoqZMv2t8TTQRBgKZ1W6iurgfHyqWwzpgGXb9BkBs2Ubo0IiKiUitzyhRF\nEampqQysFLXS1BIeshjRzajBe1YnZufZ8Ycv+ldPpMQkGO+8D5oOneFYthiOd95G0O1WuiwiIqJS\nid5u60QhJAoCOhu0aKRR412rE9Ozrcg0aNDVqIU6mlddRRHazt2gqlMXjmWLYX35eeiHjoIqrZrS\npRERUYg89dRTSpdQLrg8SnSBRJWEO+INGBGvx2anB9OyrTjg9ipdVpmpqmXAdP9jkKplwDZ7Jtyb\nN/EkLSIiiipcaSX6G0EQ0Fgro66sxjqbE6/m2dFEq0Zfkw4JUXzJVEGrhWHICLhr1YHzg3fhO/or\n9P2GQNBqlS6NiEgx+SE8mS7f7weE6P09EekYWokuQycK6G/Wo4VOxsoCJ57JLkQfswF94qN7hVLT\nvBVU1TJgX7IQ1ldegGHEOEiVKitdFhFRudJKEq7TabA63x7ScXunWEI6Hv0PQyvRv0hXq/BwohHf\nOjxYW+jA97+dwACjFmlS9O51lSpXgem+R+B4dxmss1+EfsBtkBtd+ip3RESxyKjVYHJN7u+PJtzT\nSlQCoiCgvUGDJ1PjkSqr8dy5Arxf6IArEL2rroJWC/2w0dB26wnH8rfg/Gg1ghW05yQREUU+rrQS\nXYE4ScS91SrhetUfWJ5vx0+uQvQ369BQKytdWqkIggBt+86Q0qrBsXQR/KdOQj90FESDUenSiIiI\niuFKK1Ep1NPKmJxkQnOdjIX5DizIsyPfH1C6rFJT16oD032PIuiww/bKi/CfPqV0SURERMUwtBKV\nkiwI6G3SYWKiCYWBAJ7NLsR3DjcCUdpKSrRYYLz7QUjp1WGdOxPe/XuULomIiKgIQytRGVVRS3jA\nYsRNRh0+sDoxO9eGs1F6RS1BlqG/bSS0nbrC/tZ/4frqM/ZzJSKiiMDQShQCf52oNSXJDFkQMD3b\niq/srqhcdRUEAdrO3aAfPgaurz6DY+VSBH3Rf4EFIiKKbgytRCFkkUTcmWDAwDg9PrW58XKuDX9E\n6aqrfG1DmCY8CN+Rw7DNn4OAzap0SUREVIExtBKFmCAIaKmTMSXJBJ0o4LlsK76xR+deV6lKVZju\nfRjw+2GbMxP+s2eULomIiCoohlaiMImXRNwRb8AAsw4f25yYm2dHbhR2GBDNcTDeeR+kKlVhnT0T\nrgM/K10SERFVQAytRGEkCAJa6TWYnGRGEMD07ELsdHqULuuKCbIM/bDR0LRsg3OzZ8Czc5vSJRER\nUQXDiwsQlQOLJOKeBAO+drixrMCB/W4vBpr10InRcylYQRSh63Uz9GlVkbdiCfy5OdB27QFBiJ73\nQERE0YsrrUTlRBQEdDFo8XCiESe8fkzPLsRvHp/SZV0xY7uOMIy+A+5NX8PxztsI+qLvPRARUfRh\naCUqZ1XVKjyaZEJ9jRov59rwmS36WmOp69aH8a4H4Dv0C+wLX0PQ6VS6JCIiinEMrUQKkAUBg+L0\nGB2vx5d2N17Ns6Mwyk7SUqVVhemehxEoLIR13iwE8vOULomIiGIYQyuRghppZUxKMsEdDGJ6jhUH\n3dHVxF9MsMA44QGIeiOsc2bCf/qk0iUREVGMYmglUphFEvGAxYjmWhmv5tmxPsq2C4g6PQzj7oSq\nRi1YX50F7+GDSpdEREQxSPHuAevWrcPXX3+NrKwstGrVCo888kjRY8eOHcOcOXOQlZWF1NRUjB8/\nHg0bNlSwWqLwkAQBt5h1qCmrsLTAgd88PoyI18MoRsf3SkGlhn7wcLjWfwT7f+dBP2gY5MbXK10W\nERHFEMV/I1osFgwYMABdu3Ytdr/P58PUqVPRvHlzrFixAoMGDcL06dORn5+vUKVE4XedVo3HEo2w\nBYJ4PtuKrCjqLiCIInQ9+0DX+1Y4ViyB65svEYyiFWMiIopsiofW1q1bo2XLljCbzcXu37t3L9xu\nN/r16we1Wo22bdsiPT0dmzdvVqhSovKRpJLwYKIR9f7sLvCdwx1V4U/Tpj30w8bA9dnHcK5ZhWAg\nuk4wIyKiyKR4aL2c48ePo3r16hAv+PNojRo1cOzYMQWrIiofakHAkDg9Bpp1eL/QiWWFTnijKLjK\nDRrCePsEeHftgGPpIgS90XcVMCIiiiwRG1qdTicMBkOx+wwGA5zsB0kVSCu9Bg8mGnHQ7cWsHBty\no6gtluqqmjBOeBD+E7/D9sarCDjsSpdERERRTPETsS5Hp9PBbi/+S85ut0On0110bHZ2NrKzs4tu\ni6KI5OTkkNck4PzlKiVJCvnY0UIQhAr9/v967+X5M7hKkjA5RY3/5lnxQo4V4ywm1NGoy+31/+5K\n5oBUOQ2q+x5B4YJXYX91FkzjJ0CyJIa5wvBSYg5EGn4OcA5U9DlAyojY0Jqeno5Vq1YhEAgUbRE4\nevQo2rVrd9Gxq1atwoIFC4pujxw5EhMmTAh5TZIkAUFctP+2opFlWekSFFfecyABwBOJFiw/k4PZ\n2fkYWjkJN1riIAhCudbxlyuaAwkJSHj8GZx+dRass2egyoOPQZNxVfiKKyf8HODnAOcA5wCVL8VD\nq9/vh9/vRyAQQCAQgMfjgSiKaNCgAWRZxurVq9GnTx9s374dx44dQ5s2bS4ao2/fvmjfvn3RbVEU\nkZcX+qvz+P1+QJRQWFh4/r8rIIPBcNEKeEUiSRLMZrNic6CXVkJKghFvn8nGwQIrhsQboC7n4Fra\nOaAbOR6Bd5fh92lPwTRyPOS69cNQXfgpPQciAT8HOAfCNQcSEhJCPibFDiGo8GnJy5cvx8qVK4vd\n16lTJ9x///3IysrC3LlzkZWVhZSUFNx+++0l6tN64VaBUFr20x64RRXGNW5QYT+oTCYTrFar0mUo\nRpIkJCQkIC8vT9E58LvXhzfy7DBLIsbFGxAvld/29LLMgWAwCNdnH8P99efQ9x8CuVnLEFcXfpEy\nB5TEzwHOgXDNgaSkpJCPSbFD8dAaDgyt4cNfVpHzy8oaCGBhnh1n/QGMjTeghlw+fzgJxRxwb9sM\n5+p3oO3SHZrMGxXb5lAakTQHlMLPAc4BhlZSQsR2DyCif2YSRdxjMaKhVo3ZuTZsdbiVLqnENC3b\nwDDqdrg2fAHne8sRrKC/+ImIqOQYWomimCQIGGjWo79Zh5WFTrxX6IA/Sv54oq53DYx3PQDvL/th\nX/gagi62syMiostjaCWKAW30GtxrMWKXy4tX8+ywRclVqFRVq8F0z8MIFOTD+uosBPJDfwIlERHF\nBoZWohhRU1bh0UQTnIEgXsyx4aQ3Ov7kLlosME54EKLBCOvsGfCd/F3pkoiIKAIxtBLFkARJxAOJ\nRtRQS5iZa8UuV3RcPlXU6WEYexfUderC9uoseH/eq3RJREQUYRhaiWKMLAgYHqdHD6MWi/MdWGt1\nIhAF+1wFlQq6gUOh7dQV9sUL4P72G8RgcxMiIiolxS8uQEShJwgCuhi0qKKS8Ga+Ayd9foyI00Mv\nRvb3VEEQoO1yI8SkZDhWLoX/7Fnobu4HgZeLJCKq8CL7NxgRlUl9jRqPJhqR6w/gxRwbTvuiY5+r\n3KgpjHfeB+/en2Bf+BoCTofSJRERkcIYWoliXLJKwsMWE6qqJczIseLHKNnnqsq4CqZ7H0HAWgjb\n7JnwnzurdElERKQghlaiCkAjChgdp8eNBi3ezHfgQ6szKvq5ihYLTHc/CDE5GbbZM+A9dEDpkoiI\nSCEMrUQVhCAIyDRqcXeCAdscHryaZ4fVH/n9XAWtFoaR4yG3agP7f+fB/e0GnqBFRFQBMbQSVTBX\na9R4LMkEdzCI53Os+M3jU7qkfyWIInQ9+kA/aBicH6+B891lCHq9SpdFRETliKGVqAJKkETcbzHi\nOq0aL+fa8JXdFRWrl3KTZjBOeADewwdhm/cyAgX5SpdERETlhKGVqIJSCwIGmPUYHqfHJzYXFuTb\n4YiCy7+qqqbDdN+jgFoN68vPw/fbr0qXRERE5YChlaiC+//27jw+qvre//jr7LMlIZBIwh6WsAYC\nARRwAQRciyK19tbWh/TR36+/9qott73Vttdb6tLaa/u4tdbboteiV1tr3ZeqRREXEK0CQVBwqSgi\nWAgQssw+c35/BHNFreufZCIAACAASURBVCxOMpPk/Xw85hFnOJnz+WY+mbw98z3fMzno8r0+RezJ\nZPlpQ9eYLmAWFRH5+sU4tXW0/PZXxJ/RhQhERLo7hVYRoa9t8Z0+RYzz2qYLPNYSL/iraBmWReis\nzxP64leIP/oQ0duX4cfj+S5LREQ6iEKriABtl389ryTEV3uFeLI1wa/2trC3C6wu4E6aQtEl3yWz\nYzvN111LZueOfJckIiIdQKFVRA5SG3D5flkRhmHw04YmXowlC/6jd6uyH0Xf+h5W//40/+paEn9d\nU/A1i4jIkVFoFZGPKbVMLi4Nc0okwO/3R/nd/igtBX6SlhEIEDp/EcEzFxC7506if7xN0wVERLoR\nhVYR+USmYTAnHOB7fYrYnc5yVUPhXwLWMAy8GScSueQ7ZN7Z2ra6wPZ3812WiIjkgEKriHyqfo7F\nv/aJcFLI45bGKNfvbGB/gc91tfsPpOjbl2INrqLl+p8Tf+oJ/AI/UiwiIp9OoVVEDskyDE6LBLi0\nTxF7Uxmuamji2WiioFcYMAIBwv90QdvqAk88RuuNvybbuC/fZYmIyFFSaBWRw9bPsbh84DGcEQny\nQHOMX+xtYVuqsNd1dSdOpvhffoCfzdL8i5+QXPeiTtISEemCFFpF5IiYhsHMsMe/lRVTZplcu6eF\nP+yP0lTAUwbM3r2J/L9L8GafQvTO3xP9n5vJtjTnuywRETkCCq0iclR6WSaLeoW5pHeEd1Jprmho\n4i8tcZIFehTTME0Cs+ZQ9O3vkd27h+Zrrya5YV2+yxIRkcOk0Coin8kI1+bSPkUsLArydDTBFbub\nWB1NkCnQ8GpV9iNyyXfxTphJ9A+30nrrTWSb9ue7LBEROQSFVhH5zEzDYFrI40dlxZwQ8ri/OcaV\nDc08X6Dh1bAsAnNOpejbl5Ldv5+m/7iKxPOrtcKAiEgBs/NdgIh0H55pcEokwAkhl5WtCe5ujvFo\na4KTwx7HBV1cw8h3iQexKvsRuehfSK5+mthD95Jc+1dC55yHVdkv36WJiMhH6EiriORcyDQ5oyjI\nFeXFTAu6PNIS5/JdTTzUHKOxwE7YMkwT74RZFP/rv2GGwzT/5zXEHrpPV9MSESkwOtIqIh0mZJqc\nGgkwO+zxYizJU9EEj7cmqPEcZoRcRrk2ZoEcfTV7lRK+8P+SenUjsfvvJrn+JYJnno0zcTJGgdQo\nItKTKbSKSIdzDYMZIY/pQZc3kmlWxZLcuK+ViGkwJegyJeDSz7HyXSYAzpga7BEjSaxcQfSuP2A9\n9yzB+QuxBw3Od2kiIj2aQquIdBrDMKj2HKo9h5ZslpdiKV6MJ3m8NUGFbVLrOUwIOAywrbwe3TQc\nl8C803CnHEvszw/Qcv3PcSZOJnja57DKyvNWl4hIT6bQKiJ5ETFNZoY9ZoY9dqczrI+n2JBI8Vhr\nghLTYLTnMMq1qXZtiq38TL83S3sT/vIi0sefROzh+2n62RUETphF8cLz8lKPiEhPptAqInlXblvM\ni1jMiwTYn8nySiLF5mSau5pitPo+x1gmVa5NlWMxyLGotC2cTjwSaw8ZSuSfF5PatIH4Iw/yzvOr\n8WbNwZ1xEobndVodIiI9mUKriBSUEstkeshjesgj6/vsTGd5M5lmayrNitYEuzNZTKCvbVJpW/S1\nTI6xLcosk96WSZFpdMjJXYZh4NbUEqipxXrlZfbcdxfxp5/EmzUXb/oJGK6b832KiMj/UmgVkYJl\nGgb9HYv+jsVJtB3RjGazbE9l2JHO8n46w5upDGtiSRqzbRcxsIAS06DYMik2DcKmSdg0CBoGAcMg\nYBp4RtvJYTZgGwa2ARYGptG2DqDxodtHWZZFybQTSFePIb72r+xd8yzG2r/iHjuDQN0ULNfDBKwD\nz9n2Fa1AICLyGSm0ikiXEjJNqj2T6o98Kp/yffZmsjRmsjRmfZoyWZqzPi3ZLO+ns8SyPjHfJ+FD\nwvdJ+j5pH45q1di/N7Z9HTKm7faB/Qkg8bHNDcAxwMXANdouwhA0DIKmQcgwCJsGEdMkYhoUmyYl\nlkHpgfuFsiSYiEi+KbSKSLfgGAZ9bYu+9pEtnZX1fdJAxocsPlnA98Gn7fZRlmVRUlLC/v37yWQy\ncGA7P50isXED8edXk4m2Yk6ow6qbil9cQhpI+j6pD4XmD0J0NOvTmPHZnkrRnPVpOvA4tL1B97ZM\nyiyTMtvkGMuiwjapsC1KTENHb0WkR1FoFZEezTQMXDgwF+DQIdCyTEodG8MyyXw41loeTJ6KP2ky\nqY31JJ5aQeaJR3BqavFOnIU1uOqwQ2Yi69OYzbI303ZryGTZnc7yRjLB7nSWNBAyDAY4FgPstpPT\nhjgWfSxTQVZEui2FVhGRHDJME3fCJJzxE8m89TcSzz5Jyw3/idV/IN6ME3Fq6zAc51OfwzMN+pqf\nfNQ44/s0ZLLsSGfYnsrwbirDX+NJWrI+EdNgqGMzzLWodm3625amF4hIt6HQKiLSAQzDwB42HHvY\ncDJ7GkiufobYg/cQe+g+3KnTcI+bcVQXKrA+NA1iYqDtMd/32ZPJsjWV4a1kmudjSe5rjhMyDKpd\nm9GezRjPoTRP692KiOSCQquISAez+pQRnH8OgVPPJLn+JZJrVpF46gnsESNxp07DqZmAYX/60ddP\nYxgGZbZFmW0xJdi29FZzNsvriTRbkmkebYlzR1OM/rZJTYFcdUxE5EgptIqIdBLDdfGOnY537HTS\n298l+fwqonf/EeO+P+FMmoI7+Tis/gNyEiaLTJO6oEtd0MX3fXaks2xKpNh44Kpjfay2y+bWBR0G\nKsCKSBeg0Coikgf2gIHYn/8ngp87p+3Erb+uoeWXP8Os7IdbNxV34mTMkl452ZfxofVuT4kEaMxk\nqY+nWB9PsiKaoNwymRp0mRJwKDvC1RdERDqLQquISB4Znoc7+VjcyceS2dNAau2LJJ9fTfzPD2AP\nr8aZOBm3phYjGMzZPntZJjPDHjPDHvsyWdbGk7wQS/LnljgjXJtpQZfagIOro68iUkAUWkVECoTV\npwxr3ml4c08ls+1tkuteJP7nB4jdcyfO6DE4tXU4o8dheN6hn+wwlVomc8IBTg55bE+3XV3srqYY\ndzXFODbocHzIo0JHX0WkACi0iogUGMMwsAdXYQ+uwp+/kPSbr5Fav5bo3X+ETBpn9DicCRNxRo3N\nWYA1DIOBjs1Ax+bsIp/6eIpV0QRXNTRT7dqcFHKp8RwtoSUieaPQKiJSwAzLwhk5BmfkGILpFOnX\ntpDcsI7oXX+ATAZn1Fic8bU4o8diBHIzhcA1DKYGXaYGXd5LZXg6muCWxijFlsnMkMvccDgn+xER\nORIKrSIiXYRhOzhja3DG1uCnU6Rf30Ly5Xpi9/6JaDKJXT0Sp6YWZ2wNZjiSk332dyy+VBJiflGA\n56JJnmhN8OjWHUwPuswKeZRo7VcR6SQKrSIiXZBhOzhjanDG1OBnMqTffJ3Uxnrijz5I7K4/YA8d\njjNuAs648ZilvT/z/iKmybxIgNlhj1eweHhPI0+1JpgadJkb9ijXvFcR6WAKrSIiXVzbFILROCNH\n459zHpl3tpLauIHEsyuJPXA31oCBBwLsBMy+FZ9pTVbbMDi+KMx4MrySSLO8Nc4VDc1MDjjMiwSo\nVHgVkQ6i0Coi0o0YpoldNQy7ahiBzy0gu3MHqU0bSL68nvhjD2OWleOMHY8zbjzW4CoM8+g+3jcM\ng3EBh3EBhzeSaR5rifOThmZqAw6nhQP0cxReRSS3DN/3/XwXkWtNTU14OVwS5gM3rV5DFJNvH38c\n3fDHdlhs2yadTue7jLwxDAPXdUkmk+qBHqor90C6YTex+nXENqwj8cZrmEXFBMfXEpxYR2DkGAzn\n8C4l+4964M1Ygvv3NrEpGmdqJMjZfUro5x795WkLVVfugVzpqPeBjvjbLd1HtwytDQ0NHfK8v69/\nmYRp838m1pDJZDpkH4WuqKiI5ubmfJeRN5ZlUVpayr59+9QDPVR36YFsawupVzaSeuVl0q9tActq\nWwt23IS2pbQCgX/4vYfqgbeSaR5pifNaMs2UgMNpkUC3mvPaXXrgs+io94GysrKcP6d0H5oeICLS\nA5nhCN7UaXhTp+EnEqRee5XUppfb1oJNpT7TSgRDXZuLekd4M5nm4eYYVzY0My3oclokQC+tNiAi\nR0mhVUSkhzM8D3f8RNzxE/93JYJNG4g/+tCBlQhG4NRMwKmZgFnS67Cfd7hr863eEbYk0zzYHOfH\nu5s4MeQxN+IROcq5tCLScym0iohIu4NWIljwhQMrEdQTf3oFsfvvwhoyFKYcS7Z69GEtpWUYBqM9\nh1GuTX0ixcPNcZ6LJZgTDjAz5OGZusKWiBwehVYREflEB69EcA6Z7dtIvVxPy1MrSN91B9bAwW2X\nkx0/Eat3n09/LsNgYsBlvOfwQizJIy1xno4mOD0SYFrQxdLlYUXkEBRaRUTkkAzDwB44GHvgYCLn\nnU/ja1tIvbye5AvPEX/4/gMBdhLuhImfegTWMgymhzwmB12ebk3wQHOcla0J5hcFGO85n2kNWRHp\n3hRaRUTkiBiGgd1/AHb/AQROPZPszvdIblhP8vlVxB++D2tw1f8G2H8wB9Y1DOZGAkwPuSxvSbCs\nMcpAx+LsoiDDXP1pEpGP0zuDiIgcNcMwsPoNINivLcBmdmwntWEdyVVPEX/oXqyqYbi1dTgTJn7i\nKgRh02RBcZCTwh4PN8f45d4WajyH+UUBKrrRMlki8tkptIqISE60HYEdiN1/IIHT5pN59x1S9WuJ\nP/EYsfvvwh4xCndiHc648RiB4EHf29syuaBXmNmpDA80x/jJgWWyTo8EKNEyWSKCQquIiHQAwzCw\nBw3BHjSEwJkLyGz9G8n6tcQevJfo3X/EGT0WZ9LktgsZfOhKXAMci3/uHWFLIsUDzXF+3NDE7JDH\nyeEAQa00INKjKbSKiEiHMkwTe9gI7GEj8M8+l/TrW0iuf4noH28Dw8CtqcWZOBl7eDXGgfVbR3kO\n1a7N2niKh1virIomOTUSYEbIxdHJWiI9kkKriIh0GsOy2o6yjh6Ln0ySenUjyfUv0XrzbzBCYZza\nSbgTp2ANHIRpGEwJutQGHFZFkzzWEmdlNMEZkQCTAw6mwqtIj6LQKiIieWG4Lm5tHW5tHdlolNTL\n60mtf4mWVU9j9inDmTgZd9JknPK+zAp7HBd0WdEa549NUZ5oNflcJMg4z9YyWSI9hEKriIjknRkK\n4R03A++4GWQb95GsX0tq3UskHn8Ua8BAnElT8CZM4sySXpwU8nisNc5/N7Yy2LE4MxKg2nMOvRMR\n6dIUWkVEpKCYvUoJzJxDYOYcMn9/n+S6F0mufob4Q/dhDx2BO2kyC2tqmV1WxCOtCa7f10q1a3NG\nJMBQrfEq0m3pt1tERAqW1beC4Gmfa1sDdtvbJNevJf7YQ8TuvRNv1BjOq61jzsixPJrM8p97Wxjt\n2pxRFGCwoz9vIt2NfqtFRKTgGYaBPbgKe3AV/ucWkP7bG6Tq1xK9506CmQznjR3H7LrjeKLvQH6+\np4Uxns1p4QBDdORVpNvQb7OIiHQphmXhVI/CqR5F8JwvkN6ymeSGtfS67b/5vGkyc+rxPFk7lV8k\n0ox0bU6JBBjuWDphS6SLU2gVEZEuy7AdnHHjccaNb1tCa/MmKuvX8YXfXMtJ5RU8O/sMftW3P0Mc\ni3mRIGM9W0tliXRRCq0iItItGK6LO2ES7oRJ+IkEoc2bGLDheWbt3MGquhncPKaWPn6Gk4vDTCkK\n6SIFIl2MQquIiHQ7hue1rwE7NJFg4JZXOeW5v/BsoJj7aup4cF8z0+PNnFjZl17FxfkuV0QOg0Kr\niIh0a4bn4U6YSMWEiXw+neK0N97ghT17WdWnkhXNScZtWcsJ2STDh1Rh9e2rua8iBUqhVUREegzD\ndoiMHsPJwMxMhs3vvcczxSXcUNybY3bvYuqa56izoGTESOxhIzBcN98li8gBCq0iItIjWZbFuEGD\nGAfszWRZZcPTpX141DAY88ZmJj61lJGWiXtgpQKzsp+OworkkUKriIj0eL0tk/nH9OYM32dzIs3z\nwVpuHzmOcCpJzZubqfnD/zCgtQlneDVu9WhSdVPwbV06VqQzKbSKiIgcYBkG4wIO4wIO0WyWdfEU\nL4XreG5MLaXpJGN3bGPM+hfp/6ffY5f0who6HHvoMOyq4ZjHaD6sSEdSaBUREfkEIdPk+JDH8SGP\nxkyW+niK+uBIVg0aTpFhMKplHyO2vkHVs08TuPuPGKEw1uAq7CFVWIOGYA8chBEI5nsYIt2GQquI\niMgh9LJMZoY9ZoY9Yhi8ZTm8sMfhnkgvkuOmMMjwGdG8j6ptWxnw6gbM5Y9ANot5TAXWwEHYAwZh\nDRiIVdkfw/PyPRyRLkmhVURE5AhELJOTSosZT4ZkOs3bqQxbEiles8t5MtIbxtQxyDYZEm9l4O73\n6ffWGxQ99yz+7r8DYJYfg9VvAFZlP6zK/lgV/TBKSzW1QOQQFFpFRESOkmUYDHNthrk2ZwBJ32dr\nMs1bqQxvmSbP9wsRq6wicsIpDDAN+sVbqdizi/L33qX3llexVvwFkknwPKy+lVjH9MXsW9H2tbwv\nZp8yDMvK9zBFCoJCq4iISI64hsFIz2Gk17aygO/7NGSyvJPKsD2d4T2ziLVuiP19B2NMOp5S06Dc\nz1AWi1K6fy+9dv+dkne3U7R6FaHGvZimgdm7D2ZZOWZZOVafcsw+fTD7lGP27o3haB1Z6TkUWkVE\nRDqIYRiU2xbltsXkDz0ezWZ5P51lVzrDrkyWBs/j3UgJeysG0zJuCgAWUJzNUJRKUBRtJdzSRHj/\nPoKbXiXU3EQoHiVkmoQCAcKhEKHiItySUszS3m23XqUYgUBexi3SERRaRUREOlnINBnqmgx1P/5n\nOOn77Mtkacxk2Z/12Z8J01xaSnM2y86sT0s2S2smS2vWJ/mRebB2Ok0gEcfb24r3/l68dIqA7+OZ\nBkHTxHNsgo5DIBAgGAwSDIUJOjZB0yBgGIQMg6BpYGt+rRQghVYREZEC4hoGfW2Lvvah57KmfZ9o\n1ifm+8TavxYTi8eJxaLEY3HiySSxdJpYJsO+ZJp4KksikSYRTZJoiRP3AmSsg+OAm80SwidkmURs\nm7BlEjENikyTItPguEAQXVpBOptCq4iISBdlGwbFlkHxR/8h6ELpxx5t52ez+M1NZBsbyTa8S6Jp\nP7HmZqLRKK3xONFkkqgPsUCAaDBMtKSU/cUl7AgX8bfe5TTG4nyuT0mHjk3koxRaRUREehjDNDFK\nemGW9ILBQ3CBoo9s42cyZPc34u/bR7ZxL9l9u8i+8xq3HDOIeK9eoNAqnUyhVURERD7GsCys3n2g\nd5+DHjc3bAL8/BQlPZqZ7wJERERERA5FoVVERERECp5Cq4iIiIgUPIVWERERESl4Cq0iIiIiUvAU\nWkVERESk4Cm0ioiIiEjBU2gVERERkYKn0CoiIiIiBU+hVUREREQKnkKriIiIiBQ8hVYRERERKXgK\nrSIiIiJS8BRaRURERKTg2fku4HC0tLRwww03sG7dOoLBIAsWLOCss87Kd1kiIiIi0km6RGhdunQp\nqVSKZcuWsWvXLi6//HIGDBhAXV1dvksTERERkU5Q8NMD4vE4q1ev5itf+QqhUIghQ4Ywb948Hn/8\n8XyXJiIiIiKdpOBD63vvvYfv+wwePLj9saqqKrZt25bHqkRERESkMxX89IB4PE4oFDrosXA4TCwW\na7/f0NBAQ0ND+33TNCkvL895LQYGG0vLWblxE77v5/z5uwLLtsmk0/kuI28Mw8BxHFKplHqgh1IP\nqAd6eg9sLqvghD3vY1lWvkuRHqbgQ2sgEDgooAJEo1GCwWD7/XvuuYebbrqp/f6FF17IRRddlPNa\nqovCvB5r5WnTy/lzdxk+YBV823SsDKAeyHcV+aUeUA/04B4obW1heFGE0tLSfJciPUzBv+v0798f\ngG3btjFo0CAAtm7d2v7fAAsXLuSkk05qv2+aJvv27ct5LdNHj+LU4mKamprIZDI5f/6uIBwO09ra\nmu8y8sayLIrVA+oB9YB6QD3QIX9nFYTl0xR8aA0EAsyYMYPbbruNxYsXs3v3bpYvX863vvWt9m3K\nysooKytrv9/Q0NChbySZTKbHvlH5vt9jx/5h6oGeOfYPUw/0zLF/mHqgZ45d8qfgQyvA17/+dX79\n619z4YUXEgwGWbhwoZa7EhEREelBukRojUQiXHbZZfkuQ0RERETypOCXvBIRERERUWgVERERkYKn\n0CoiIiIiBU+hVUREREQKnkKriIiIiBQ8hVYRERERKXgKrSIiIiJS8BRaRURERKTgKbSKiIiISMFT\naBURERGRgqfQKiIiIiIFT6FVRERERAqe4fu+n+8iuoqGhgbuueceFi5cSFlZWb7LkTxQD4h6QNQD\nIvmhI61HoKGhgZtuuomGhoZ8lyJ5oh4Q9YCoB0TyQ6FVRERERAqeQquIiIiIFDxryZIlS/JdRFcS\nDAaZPHkyoVAo36VInqgHRD0g6gGRzqcTsURERESk4Gl6gIiIiIgUPIVWERERESl4dr4L6CpaWlq4\n4YYbWLduHcFgkAULFnDWWWfluyzJgSN5bTdt2sRvf/tb3n//fQYNGsTFF19MVVUVACtWrODPf/4z\nO3bsIBAIMHXqVBYtWkQwGOzM4chRyFUPfNgvf/lLnnzySf7rv/6LAQMGdPQQ5DPKZQ/s2rWLm266\niZdffhnLspgyZQqLFy/urKGIdFs60nqYli5dSiqVYtmyZSxZsoS7776btWvX5rssyYHDfW2bmpq4\n+uqrOeecc7jjjjs48cQTueqqq0ilUgAkEgm++tWvctttt/GrX/2KnTt3smzZss4ejhyFXPXABzZu\n3Mjf//73zipfciBXPZBOp/n3f/93Ro0axS233MKtt97K/PnzO3s4It2SQuthiMfjrF69mq985SuE\nQiGGDBnCvHnzePzxx/NdmnxGR/LarlmzhsrKSmbPno3jOJx11ln4vk99fT0Ap59+OuPGjcNxHIqL\niznllFPYvHlzZw9JjlAuewAglUpx44038o1vfKMzhyGfQS574Mknn6S4uJiFCxcSDAZxHIdhw4Z1\n9pBEuiWF1sPw3nvv4fs+gwcPbn+sqqqKbdu25bEqyYUjeW23bdt20EeAhmEwZMiQf9gHmzZtYtCg\nQbkvWnIq1z1wzz33MGnSJL32XUgue2DLli1UVFRwxRVXcP755/O9732PLVu2dPwgRHoAhdbDEI/H\nP7YWXzgcJhaL5akiyZUjeW1jsRjhcPiwtn3++ed55plnOP/883NbsORcLntgx44drFy5ki9+8Ysd\nV7DkXC57oKGhgWeeeYZTTz2VW2+9lTlz5nDllVfS0tLScQMQ6SEUWg9DIBD42JtXNBrVCTbdwJG8\ntsFgkGg0etBjra2tH9u2vr6e66+/nh/+8If069cv90VLTuWyB37zm99wwQUX6L2hi8llD3iex6hR\no5g6dSq2bTNv3jwikYimConkgELrYejfvz/AQR8Vbd26VR//dQNH8toOGjSIt956q/2+7/u8/fbb\nB227YcMGrr32Wi699FLGjh3bgZVLruSyBzZs2MDSpUu54IILuOCCCwC47LLL+Mtf/tKRQ5DPKJc9\nMGTIkI4tVqQHU2g9DIFAgBkzZnDbbbcRjUZ55513WL58OXPnzs13afIZHclrO23aNHbu3MnKlStJ\npVI8+OCDANTW1gJtZ4z/7Gc/4zvf+Q7jx4/v1HHI0ctlD9x6661cd9117TeAH/zgB8ycObPTxiNH\nLpc9MGvWLN58803Wrl1LJpNhxYoVtLa2Mnr06E4dk0h3pMu4HqaWlhZ+/etft6/hd84552id1m7i\n017bL3zhC/zoRz9qP2q6ceNGli5d2r4+40UXXcTQoUMB+OEPf8grr7yC67rtz11eXs4NN9zQ+YOS\nI5KrHvio+fPna53WLiKXPfDCCy9wyy23sHfvXgYOHMjXvvY1Ro0alZdxiXQnCq0iIiIiUvA0PUBE\nRERECp5Cq4iIiIgUPIVWERERESl4Cq0iIiIiUvAUWkVERESk4Cm0ioiIiEjBU2gVERERkYKn0Coi\nIiIiBU+hVUS6pcbGRpYsWcKrr76a71JERCQHFFpFpFtqbGzkxz/+sUKriEg3odAqIjnh+z6JRKJD\n9xGLxTr0+UVEpHAptIrIUbnwwgsZN24cjzzyCBMmTMDzPB566CEaGxv55je/SWVlJZ7nUVdXx/Ll\nyw/6Xt/3ueKKK6ioqCASiXDuuefyxBNPYBgGTz31VPt2hmFwzTXXcOmll1JRUcExxxzT/m9r1qxh\n9uzZhMNhSkpK+NKXvsSuXbsAePvtt6mqqgLg3HPPxTAMDMPg7bff7vCfi4iIdAyFVhE5ajt27OCS\nSy5h8eLFPPbYY4wfP565c+fy8MMPc/XVV/Pggw8yZswYzjjjDDZu3Nj+fddffz1Llizhwgsv5N57\n72XYsGF87Wtf+8R9XHfddbz++uvcfPPN3H777UBbYJ05cyYlJSXceeed3Hjjjbz44oucddZZAFRW\nVnLvvfcC8JOf/IQ1a9awZs0aKisrO/gnIiIiHcXOdwEi0nXt27ePRx99lGOPPRaAZcuWUV9fz4YN\nGxgzZgwAp5xyCm+88QZXXnklf/rTn8hkMlxzzTUsWrSIa665BoB58+bR0NDAzTff/LF99O7dm3vv\nvRfDMNofu+yyy5g8efJBj9fU1LQf+T399NOZOHEiACNGjOC4447r0J+DiIh0PB1pFZGj1qdPn/bA\nCrB8+XJqamqorq4mnU633+bOncuLL74IwPbt29m5cyfz588/6Lk+OEr6UaeddtpBgTUajbJ69WrO\nPfdcMplM+z6qq6sZOHBg+35ERKR70ZFWETlqffv2Peh+Q0MD69evx3Gcj21rWRYAO3fuBKC8vPyg\nf//wfNVP28e+ZlZrqAAAAmdJREFUffvIZDIsXryYxYsXf2z7d9999/AHICIiXYZCq4gctQ8fAYW2\nj/LHjx//iR/zf+CDeaW7d+8+6PEPTqI61D569eqFYRj84Ac/4Oyzz/7Y9mVlZYdVu4iIdC0KrSKS\nM3PmzOGRRx6hX79+9OvX7xO3GTBgABUVFTzwwAMHTQm4//77D2sf4XCYadOmsXnzZq666qp/uJ3r\nugDE4/EjGIGIiBQqhVYRyZkLLriApUuXMnPmTL773e9SXV1NY2Mj69evJ5lM8tOf/hTLsvj+97/P\nt7/9bfr27cusWbNYuXIlTzzxBACmeeip9tdeey2zZ8/mvPPO44tf/CKlpaVs376dxx9/nEWLFjFz\n5kwqKiro1asXd9xxB1VVVXiex/jx49vDrIiIdC06EUtEcsbzPJ588knOPPNMrr76aubNm8c3v/lN\nXnrpJY4//vj27S6++GJ+9KMf8bvf/Y4FCxbw6quvcu211wJQUlJyyP1Mnz6dVatW0dLSwqJFizj9\n9NO54oorCIVCDB8+HGgLv8uWLWPr1q2cfPLJTJkyhR07dnTMwEVEpMMZvu/7+S5CROTyyy/nF7/4\nBXv27CEYDOa7HBERKTCaHiAinW7z5s3cfvvtTJ8+Hdd1eeqpp/j5z3/ON77xDQVWERH5RDrSKiKd\n7p133mHRokXU19fT3NxM//79+fKXv8ySJUuwbf2/tIiIfJxCq4iIiIgUPJ2IJSIiIiIFT6FVRERE\nRAqeQquIiIiIFDyFVhEREREpeAqtIiIiIlLwFFpFREREpOAptIqIiIhIwVNoFREREZGCp9AqIiIi\nIgXv/wNa19DJ/n8Z+QAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11b30ce10>"
+       "<matplotlib.figure.Figure at 0x11c9bae10>"
       ]
      },
      "metadata": {},
@@ -5497,10 +6024,10 @@
     {
      "data": {
       "text/plain": [
-       "<ggplot: (-9223372036556999883)>"
+       "<ggplot: (296976553)>"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5513,19 +6040,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {},
+   "execution_count": 96,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "type\n",
-       "standard    0.017105\n",
-       "temporal    0.031316\n",
+       "standard    0.015000\n",
+       "temporal    0.013929\n",
        "Name: regret, dtype: float64"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5538,29 +6069,40 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "source": [
-    "We can see that, for this task, unlike the regression task, temporal CV had a larger average regret. "
+    "We can see that, for this task, the model regret was quite similar. Therefore, they tended to selected models that performed similary. However, as we saw above, temporal CV's estimates of how those model would perform tended to be much better. "
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "## Post Analysis of Classification Results"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
-    "Throughout this notebook, we have argued for usage of temporal CV in time series data. This real world classification example demonstrates some of the reasons as to why. First is the question of computational power. In this example we modeled 38 years of prisoner recidivism, a dataset containing about 800,000 rows and 500 columns. Standard CV required the training of 6552 models, which lasted around 8 hours of training time on a 128 core machine on AWS. Temporal CV required only 312 models, which means with roughly the same computational time we could have tested 168 models instead of 8. And as we can see with all of the analyses above, temporal CV generally results in similar precisions and decisions as temporal CV. "
+    "Throughout this notebook, we have argued for usage of temporal CV in time series data. This real world classification example demonstrates some of the reasons as to why. First is the question of computational power. In this example we modeled about 30 years of prisoner recidivism, a dataset containing about 800,000 rows and 500 columns. 5 Fold CV required the training and testing of 840 models. Temporal CV required only 192 models, which means with roughly the same computational time we could have tested 26 models instead of 6. And as we can see with all of the analyses above, temporal CV generally results in similar decisions as temporal CV on which model to use, but the estimates as to how those models perform tended to be much better from temporal CV."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Further, there are specific situations in which standard CV can result in biased predictions. Consider the case of a sudden change in the underlying pattern that we are trying to model, as we saw around 1995. At the test year of 1995, neither CV system knows that the change is coming, as therefore the models that they select should to perform poorly when trained on all the years prior and tested on 1995. But the subsequent years will differ. \n",
     "\n",
@@ -5569,7 +6111,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Of course, in practice, this bias can be overshadowed by the variance of the models and selection algorithms themselves. In this task, for example, the standard CV algorithm actually predicted precisions that tended to be somewhat lower than the actual precisions (see the first graph in 'Results Comparison').\n",
     "\n",
@@ -5578,14 +6123,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
-   "metadata": {},
+   "execution_count": 104,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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qdv3LlqKPLojo7gnakGGouXkEVq0k5Tw5IEcIIZJByMXuk08+yV133cUvf/nL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\nmBZhFwAAAKb1v2l8Z17e/VTXAAAAAElFTkSuQmCC\n",
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0zVsR2Pc6hB6I6Vxk7glunou8XjhS8sJFcG7/a2TfdS/UhtXw/f5/0PfVL2P4f38H3t9v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\nMG9uZwOlogpQVOi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e/imEoYMp8W/plixGiwvM4Qz7aVM4kjPjYqnE25QME0LiLuqMRpZl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\nMBERERElrP8HbrdDVrL9xFkAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11a84ef28>"
+       "<matplotlib.figure.Figure at 0x11c40b2b0>"
       ]
      },
      "metadata": {},
@@ -5594,58 +6143,57 @@
     {
      "data": {
       "text/plain": [
-       "<ggplot: (-9223372036561099320)>"
+       "<ggplot: (-9223372036556356657)>"
       ]
      },
-     "execution_count": 217,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "(ggplot(c_temp_vs_std[(c_temp_vs_std.test_year >=1993) & (c_temp_vs_std.test_year <= 2003)], aes(x='test_year', y='diff_in_prec', colour='type')) +\n",
+    "(ggplot(c_temp_vs_std[(c_temp_vs_std.test_year >=1993) & (c_temp_vs_std.test_year <= 2010)], aes(x='test_year', y='diff_in_prec', colour='type')) +\n",
     "    geom_line() +\n",
     "    xlab('Year') +\n",
-    "    ylab('Error in Precision Estimate')\n",
+    "    ylab('Error in Precision Estimate') +\n",
+    "    geom_hline(yintercept=0, linetype='dashed')\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
-   "metadata": {},
+   "execution_count": 105,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "type\n",
-       "standard    0.034771\n",
-       "temporal   -0.000303\n",
+       "standard    0.068333\n",
+       "temporal    0.023062\n",
        "Name: diff_in_prec, dtype: float64"
       ]
      },
-     "execution_count": 218,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# The mean difference in predicted and actual precisions for the years visualized above.\n",
-    "c_temp_vs_std[(c_temp_vs_std.test_year >=1993) & (c_temp_vs_std.test_year <= 2003)].groupby([\"type\"])[\"diff_in_prec\"].mean()"
+    "c_temp_vs_std[(c_temp_vs_std.test_year >=1993) & (c_temp_vs_std.test_year <= 2010)].groupby([\"type\"])[\"diff_in_prec\"].mean()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "deletable": true,
+    "editable": true
    },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Given that this problem is from real-world data, the trend observed in the years above is impossible to attribute directly to the error estimating methods of standard CV, given the variance in the performance of the models and the underlying data. But the motivating factor remains, that in cases of changes in trends on underlying data, standard cross validation will introduce error in its predictions of the out-of-sample performance of the models being tested. \n",
     "\n",
@@ -5669,7 +6217,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.6.0"
   }
  },
  "nbformat": 4,
diff --git a/curriculum/3_modeling_and_machine_learning/machine-learning/data_cv/KFold_cv_results.csv b/curriculum/3_modeling_and_machine_learning/machine-learning/data_cv/KFold_cv_results.csv
new file mode 100644
index 00000000..15b72818
--- /dev/null
+++ b/curriculum/3_modeling_and_machine_learning/machine-learning/data_cv/KFold_cv_results.csv
@@ -0,0 +1,840 @@
+1981,decision1,1,0.67,0.6
+1981,decision1,2,0.66,0.59
+1981,decision2,2,0.53,0.82
+1981,decision1,3,0.64,0.61
+1981,decision1,4,0.63,0.61
+1981,decision2,3,0.51,0.84
+1981,decision2,0,0.5,0.92
+1981,decision2,4,0.51,0.86
+1981,decision2,1,0.56,0.77
+1982,decision1,1,0.73,0.74
+1982,decision1,2,0.62,0.77
+1982,decision2,2,0.45,1.11
+1982,decision1,3,0.77,0.76
+1982,decision1,4,0.67,0.8
+1982,decision2,3,0.39,1.16
+1982,decision2,0,0.45,1.1
+1982,decision2,4,0.41,1.18
+1983,decision1,1,0.64,0.94
+1982,decision2,1,0.4,1.1
+1983,decision1,2,0.72,0.93
+1983,decision2,2,0.45,1.45
+1983,decision1,3,0.67,0.95
+1983,decision2,3,0.52,1.28
+1983,decision1,4,0.63,1.05
+1983,decision2,4,0.31,1.37
+1983,decision2,0,0.41,1.54
+1984,decision1,1,0.64,1.09
+1984,decision1,2,0.7,1.14
+1984,decision2,2,0.51,1.56
+1983,decision2,1,0.47,1.49
+1984,decision1,3,0.68,1.22
+1984,decision2,3,0.54,1.65
+1984,decision1,4,0.66,1.08
+1985,decision1,1,0.66,1.28
+1984,decision2,4,0.44,2.1
+1984,decision2,0,0.42,1.8
+1985,decision2,2,0.47,1.78
+1985,decision1,2,0.76,1.73
+1984,decision2,1,0.35,2.19
+1985,decision1,3,0.61,1.4
+1985,decision2,3,0.39,1.97
+1985,decision1,4,0.64,1.61
+1986,decision1,1,0.74,1.78
+1985,decision2,4,0.43,2.42
+1985,decision2,0,0.42,2.59
+1986,decision1,2,0.58,1.75
+1986,decision2,2,0.45,2.95
+1986,decision1,3,0.7,2.06
+1985,decision2,1,0.48,2.69
+1986,decision2,3,0.53,2.6
+1987,decision1,1,0.63,1.88
+1986,decision1,4,0.72,2.1
+1986,decision2,4,0.53,2.6
+1987,decision1,2,0.68,2.37
+1987,decision2,2,0.49,3.91
+1986,decision2,0,0.42,3.27
+1987,decision1,3,0.6,2.86
+1988,decision1,1,0.78,2.62
+1987,decision2,3,0.51,3.59
+1986,decision2,1,0.44,3.28
+1988,decision2,2,0.66,2.94
+1987,decision1,4,0.78,2.62
+1988,decision1,2,0.73,2.69
+1987,decision2,4,0.44,4.38
+1988,decision2,3,0.48,3.68
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diff --git a/curriculum/3_modeling_and_machine_learning/machine-learning/data_cv/cv_results.csv b/curriculum/3_modeling_and_machine_learning/machine-learning/data_cv/cv_results.csv
deleted file mode 100644
index 50c33c63..00000000
--- a/curriculum/3_modeling_and_machine_learning/machine-learning/data_cv/cv_results.csv
+++ /dev/null
@@ -1,6558 +0,0 @@
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-1950,1989,1983,rf1,0.66
-1950,1995,1980,extra2,0.66
-1950,1984,1973,rf2,0.79
-1950,1986,1973,rf1,0.84
-1950,1976,1973,decision2,0.45
-1950,1981,1973,decision1,0.7
-1950,1980,1980,rf2,0.73
-1950,1985,1973,rf1,0.79
-1950,1983,1979,decision2,0.49
-1950,1985,1980,decision2,0.46
-1950,1983,1976,extra2,0.67
-1950,1987,1985,rf1,0.59
-1950,1982,1978,logistic1,0.71
-1950,1990,1980,decision2,0.57
-1950,1993,1985,extra2,0.56
-1950,1988,1988,rf2,0.7
-1950,1993,1982,extra2,0.7
-1950,1986,1975,rf1,0.74
-1950,1991,1981,decision1,0.69
-1950,1997,1975,extra2,0.77
-1950,1992,1973,rf1,0.69
-1950,1983,1973,extra1,0.7
-1950,1977,1973,extra2,0.76
-1950,1987,1981,rf2,0.84
-1950,1991,1987,extra2,0.68
-1950,1981,1975,extra1,0.72
-1950,1978,1973,extra2,0.79
-1950,1994,1986,extra1,0.66
-1950,1991,1978,rf1,0.69
-1950,1982,1975,rf2,0.77
-1950,1981,1978,decision1,0.62
-1950,1984,1976,logistic1,0.66
-1950,1974,1974,extra2,0.64
-1950,1980,1973,logistic2,0.66
-1950,1985,1974,decision2,0.38
-1950,1989,1989,extra2,0.84
-1950,1989,1980,rf1,0.7
-1950,1982,1973,decision1,0.68
-1950,1997,1991,extra1,0.78
-1950,1989,1973,rf2,0.76
-1950,1980,1980,logistic1,0.75
-1950,1982,1982,rf1,0.81
-1950,1990,1974,extra2,0.74
-1950,1975,1973,rf2,0.74
-1950,1996,1990,extra2,0.76
-1950,1976,1975,extra2,0.76
-1950,1975,1975,extra1,0.57
-1950,1974,1974,decision2,0.44
-1950,1989,1973,rf1,0.76
-1950,1998,1976,extra1,0.69
-1950,1981,1981,decision2,0.54
-1950,1988,1973,rf1,0.76
-1950,1985,1977,decision2,0.59
-1950,1980,1974,rf2,0.75
-1950,1983,1973,decision1,0.74
-1950,1977,1973,decision2,0.42
-1950,1986,1981,logistic2,0.76
-1950,1978,1973,decision2,0.4
-1950,1987,1978,rf2,0.73
-1950,1980,1977,rf2,0.74
-1950,1993,1991,rf2,0.72
-1950,1992,1991,decision1,0.57
-1950,1989,1986,decision1,0.56
-1950,1985,1983,logistic1,0.68
-1950,1975,1975,decision1,0.63
-1950,1979,1977,extra1,0.66
-1950,1981,1973,logistic2,0.6
-1950,1975,1973,logistic1,0.74
-1950,1979,1973,extra2,0.79
-1950,1984,1983,extra1,0.66
-1950,1995,1977,extra2,0.66
-1950,1987,1975,decision2,0.51
-1950,1990,1973,rf1,0.74
-1950,1978,1976,rf2,0.7
-1950,1991,1975,rf1,0.71
-1950,1993,1976,decision1,0.6
-1950,1992,1981,rf2,0.79
-1950,1984,1973,logistic1,0.66
-1950,1989,1976,rf2,0.65
-1950,1990,1990,rf1,0.68
-1950,1982,1975,logistic1,0.69
-1950,1990,1986,rf2,0.72
-1950,1986,1984,decision2,0.57
-1950,1981,1975,decision1,0.68
-1950,1994,1983,rf1,0.64
-1950,1991,1973,rf1,0.7
-1950,1992,1988,rf1,0.61
-1950,1988,1985,decision2,0.56
-1950,1977,1976,rf1,0.69
-1950,1979,1974,rf1,0.69
-1950,1988,1976,logistic2,0.65
-1950,1975,1975,logistic2,0.76
-1950,1976,1975,decision2,0.48
-1950,1994,1973,rf1,0.71
-1950,1992,1985,rf1,0.58
-1950,1976,1973,rf2,0.85
-1950,1984,1973,extra1,0.7
-1950,1995,1993,rf1,0.67
-1950,1980,1974,logistic1,0.79
-1950,1988,1988,logistic1,0.62
-1950,1991,1984,extra2,0.67
-1950,1981,1978,logistic2,0.66
-1950,1978,1976,logistic1,0.72
-1950,1994,1989,extra2,0.84
-1950,1979,1977,decision1,0.61
-1950,1974,1974,rf2,0.71
-1950,1980,1977,logistic1,0.7
-1950,1990,1983,rf2,0.68
-1950,1987,1981,logistic1,0.72
-1950,1994,1980,rf1,0.71
-1950,1994,1976,rf2,0.64
-1950,1996,1978,rf1,0.67
-1950,1998,1973,rf1,0.78
-1950,1996,1996,rf2,0.64
-1950,1992,1978,rf2,0.64
-1950,1998,1982,logistic2,0.75
-1950,1993,1988,rf2,0.62
-1950,1976,1973,logistic1,0.82
-1950,1983,1976,decision2,0.44
-1950,1993,1973,rf1,0.67
-1950,1995,1986,rf2,0.65
-1950,1982,1973,logistic2,0.6
-1950,1974,1974,logistic1,0.41
-1950,1996,1987,extra2,0.72
-1950,1996,1973,rf1,0.73
-1950,1984,1983,decision1,0.63
-1950,1997,1981,rf2,0.83
-1950,1988,1979,decision2,0.49
-1950,1981,1975,logistic2,0.71
-1950,1980,1973,extra2,0.76
-1950,1983,1982,rf2,0.79
-1950,1994,1992,extra2,0.76
-1950,1997,1988,rf1,0.64
-1950,1997,1985,rf1,0.56
-1950,1996,1975,rf1,0.69
-1950,1983,1973,logistic2,0.59
-1950,1975,1974,rf1,0.66
-1950,1997,1973,rf1,0.72
-1950,1985,1973,extra1,0.7
-1950,1975,1975,extra2,0.8
-1950,1977,1973,rf2,0.83
-1950,1982,1979,rf1,0.65
-1950,1982,1982,extra1,0.67
-1950,1997,1997,extra2,0.7
-1950,1988,1982,decision2,0.62
-1950,1979,1977,logistic2,0.71
-1950,1984,1980,extra1,0.71
-1950,1990,1977,decision2,0.47
-1950,1989,1973,logistic1,0.6
-1950,1991,1990,decision2,0.65
-1950,1977,1976,extra1,0.68
-1950,1978,1973,rf2,0.8
-1950,1984,1973,decision1,0.62
-1950,1995,1990,rf1,0.73
-1950,1987,1978,logistic1,0.67
-1950,1990,1983,logistic1,0.6
-1950,1976,1975,rf2,0.8
-1950,1981,1981,rf2,0.78
-1950,1975,1975,decision2,0.47
-1950,1994,1973,rf2,0.75
-1950,1994,1986,decision1,0.66
-1950,1996,1981,decision1,0.73
-1950,1981,1973,extra2,0.74
-1950,1989,1976,logistic1,0.63
-1950,1993,1979,extra2,0.63
-1950,1979,1974,extra1,0.66
-1950,1977,1973,logistic1,0.82
-1950,1983,1979,rf2,0.74
-1950,1977,1976,decision1,0.61
-1950,1991,1987,decision2,0.54
-1950,1986,1978,extra1,0.7
-1950,1983,1982,logistic1,0.76
-1950,1986,1973,extra1,0.73
-1950,1984,1977,rf1,0.73
-1950,1980,1973,decision2,0.45
-1950,1987,1985,extra1,0.56
-1950,1975,1974,extra1,0.65
-1950,1976,1974,rf1,0.66
-1950,1997,1994,extra2,0.81
-1950,1990,1974,decision2,0.48
-1950,1978,1973,logistic1,0.86
-1950,1985,1980,rf2,0.74
-1950,1976,1975,logistic1,0.79
-1950,1986,1975,extra1,0.72
-1950,1988,1973,extra1,0.71
-1950,1990,1986,logistic1,0.63
-1950,1978,1977,rf1,0.74
-1950,1987,1973,extra1,0.73
-1950,1981,1978,extra2,0.75
-1950,1980,1978,rf1,0.72
-1950,1985,1973,decision1,0.64
-1950,1985,1977,rf2,0.73
-1950,1986,1981,extra2,0.83
-1950,1982,1982,decision1,0.69
-1950,1997,1991,decision1,0.6
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-1950,1989,1989,decision2,0.66
-1950,1979,1974,decision1,0.59
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-1950,1977,1976,logistic2,0.68
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-1950,1984,1983,logistic2,0.65
-1950,1982,1976,rf1,0.72
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-1950,1995,1973,rf1,0.71
-1950,1984,1980,decision1,0.65
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-1950,1989,1980,extra1,0.7
-1950,1989,1986,logistic2,0.66
-1950,1985,1984,rf1,0.64
-1950,1996,1984,extra2,0.63
-1950,1981,1973,decision2,0.45
-1950,1979,1977,extra2,0.69
-1950,1981,1975,extra2,0.77
-1950,1990,1980,rf2,0.69
-1950,1993,1982,decision2,0.59
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-1950,1984,1974,rf1,0.68
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-1950,1995,1983,rf2,0.68
-1950,1989,1983,extra1,0.64
-1950,1976,1974,extra1,0.7
-1950,1988,1973,decision1,0.64
-1950,1975,1975,rf2,0.76
-1950,1998,1985,extra2,0.58
-1950,1986,1978,decision1,0.64
-1950,1983,1979,logistic1,0.7
-1950,1981,1978,decision2,0.45
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-1950,1986,1973,decision1,0.66
-1950,1975,1975,logistic1,0.81
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-1950,1997,1978,rf2,0.75
-1950,1983,1976,rf2,0.68
-1950,1977,1974,rf1,0.67
-1950,1987,1973,decision1,0.64
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-1950,1989,1973,extra1,0.73
-1950,1982,1979,extra1,0.64
-1950,1978,1977,extra1,0.69
-1950,1992,1973,extra1,0.63
-1950,1988,1976,extra2,0.72
-1950,1976,1976,rf1,0.63
-1950,1986,1984,rf2,0.66
-1950,1979,1974,logistic2,0.75
-1950,1996,1993,rf2,0.71
-1950,1987,1985,decision1,0.62
-1950,1993,1985,decision2,0.59
-1950,1982,1982,logistic2,0.76
-1950,1992,1991,logistic2,0.64
-1950,1988,1985,rf2,0.6
-1950,1986,1975,decision1,0.68
-1950,1979,1977,decision2,0.48
-1950,1978,1974,rf1,0.7
-1950,1984,1973,logistic2,0.6
-1950,1994,1976,logistic1,0.58
-1950,1985,1977,logistic1,0.64
-1950,1976,1974,logistic2,0.81
-1950,1977,1976,extra2,0.71
-1950,1991,1978,extra1,0.68
-1950,1998,1976,decision1,0.58
-1950,1985,1974,logistic1,0.65
-1950,1985,1973,logistic2,0.58
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-1950,1980,1975,extra1,0.73
-1950,1980,1973,rf2,0.79
-1950,1993,1988,logistic1,0.61
-1950,1978,1977,decision1,0.62
-1950,1980,1978,extra1,0.72
-1950,1983,1983,rf1,0.67
-1950,1987,1975,rf2,0.76
-1950,1993,1976,logistic2,0.58
-1950,1981,1975,decision2,0.54
-1950,1991,1975,extra1,0.67
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-1950,1983,1976,logistic1,0.66
-1950,1984,1980,logistic2,0.7
-1950,1984,1977,extra1,0.68
-1950,1982,1973,decision2,0.4
-1950,1991,1984,decision2,0.62
-1950,1995,1986,logistic1,0.61
-1950,1989,1980,decision1,0.65
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-1950,1976,1976,extra1,0.63
-1950,1998,1979,extra2,0.72
-1950,1990,1990,extra1,0.7
-1950,1977,1976,decision2,0.52
-1950,1980,1975,decision1,0.65
-1950,1977,1974,extra1,0.7
-1950,1988,1982,rf2,0.81
-1950,1987,1982,rf1,0.79
-1950,1982,1979,decision1,0.55
-1950,1980,1973,logistic1,0.76
-1950,1976,1974,extra2,0.76
-1950,1996,1996,logistic1,0.4
-1950,1978,1977,logistic2,0.69
-1950,1980,1978,decision1,0.61
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-1950,1986,1984,logistic1,0.65
-1950,1992,1973,decision1,0.67
-1950,1993,1973,extra1,0.6
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-1950,1998,1994,logistic1,0.72
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-1950,1986,1978,logistic2,0.63
-1950,1978,1974,extra1,0.69
-1950,1990,1984,rf1,0.67
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-1950,1989,1983,decision1,0.64
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-1950,1979,1974,extra2,0.79
-1950,1995,1980,decision2,0.57
-1950,1996,1973,extra1,0.74
-1950,1984,1983,extra2,0.79
-1950,1995,1993,extra1,0.7
-1950,1976,1976,decision1,0.61
-1950,1977,1974,decision1,0.62
-1950,1986,1973,logistic2,0.58
-1950,1983,1973,decision2,0.47
-1950,1994,1980,extra1,0.7
-1950,1982,1976,decision1,0.58
-1950,1989,1973,decision1,0.61
-1950,1997,1975,decision2,0.58
-1950,1976,1974,decision2,0.37
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-1950,1992,1988,extra1,0.62
-1950,1981,1973,rf2,0.79
-1950,1992,1985,extra1,0.59
-1950,1990,1977,rf2,0.67
-1950,1980,1978,logistic2,0.71
-1950,1985,1984,extra1,0.68
-1950,1988,1973,logistic2,0.59
-1950,1987,1973,logistic2,0.57
-1950,1997,1981,logistic1,0.79
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-1950,1980,1975,logistic2,0.7
-1950,1987,1975,logistic1,0.68
-1950,1982,1982,extra2,0.75
-1950,1978,1974,decision1,0.56
-1950,1986,1975,logistic2,0.67
-1950,1988,1985,logistic1,0.56
-1950,1977,1974,logistic2,0.78
-1950,1994,1983,extra1,0.64
-1950,1994,1992,decision2,0.74
-1950,1981,1978,rf2,0.75
-1950,1991,1978,decision1,0.59
-1950,1996,1978,extra1,0.7
-1950,1998,1973,extra1,0.71
-1950,1979,1977,rf2,0.71
-1950,1988,1976,decision2,0.45
-1950,1987,1979,rf1,0.64
-1950,1984,1977,decision1,0.58
-1950,1984,1974,extra1,0.64
-1950,1998,1988,decision2,0.6
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-1950,1979,1974,decision2,0.49
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-1950,1989,1974,rf1,0.63
-1950,1978,1977,extra2,0.66
-1950,1995,1977,decision2,0.51
-1950,1982,1979,logistic2,0.69
-1950,1983,1983,extra1,0.64
-1950,1990,1973,decision1,0.62
-1950,1987,1985,logistic2,0.58
-1950,1995,1983,logistic1,0.57
-1950,1984,1973,extra2,0.75
-1950,1983,1980,rf1,0.71
-1950,1977,1976,rf2,0.72
-1950,1994,1986,logistic2,0.56
-1950,1994,1989,decision2,0.7
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-1950,1988,1982,logistic1,0.7
-1950,1988,1979,logistic1,0.66
-1950,1981,1973,logistic1,0.73
-1950,1981,1975,rf2,0.81
-1950,1982,1976,logistic2,0.68
-1950,1991,1987,rf2,0.65
-1950,1985,1978,rf1,0.69
-1950,1996,1990,decision2,0.64
-1950,1978,1974,logistic2,0.77
-1950,1979,1977,logistic1,0.72
-1950,1991,1973,decision1,0.6
-1950,1989,1989,rf2,0.83
-1950,1989,1986,extra2,0.76
-1950,1977,1976,logistic1,0.8
-1950,1993,1979,decision2,0.53
-1950,1992,1975,rf2,0.71
-1950,1985,1973,extra2,0.73
-1950,1989,1977,rf1,0.7
-1950,1985,1984,decision1,0.65
-1950,1985,1975,rf1,0.74
-1950,1978,1977,decision2,0.46
-1950,1997,1988,extra1,0.72
-1950,1983,1983,decision1,0.56
-1950,1976,1974,rf2,0.77
-1950,1993,1973,decision1,0.55
-1950,1997,1978,logistic1,0.66
-1950,1990,1974,rf2,0.62
-1950,1998,1976,logistic2,0.65
-1950,1977,1974,extra2,0.79
-1950,1980,1974,rf1,0.69
-1950,1991,1975,decision1,0.6
-1950,1990,1990,decision1,0.69
-1950,1984,1980,extra2,0.77
-1950,1984,1974,decision1,0.53
-1950,1982,1973,rf2,0.8
-1950,1980,1975,extra2,0.77
-1950,1982,1982,decision2,0.49
-1950,1976,1976,extra2,0.66
-1950,1983,1977,rf1,0.73
-1950,1989,1980,logistic2,0.68
-1950,1997,1985,extra1,0.55
-1950,1996,1993,logistic1,0.69
-1950,1997,1973,extra1,0.72
-1950,1984,1983,decision2,0.49
-1950,1996,1987,decision2,0.54
-1950,1980,1978,extra2,0.76
-1950,1981,1975,logistic1,0.71
-1950,1997,1997,decision2,0.58
-1950,1990,1987,rf1,0.62
-1950,1984,1977,logistic2,0.64
-1950,1991,1981,extra2,0.74
-1950,1977,1974,decision2,0.41
-1950,1989,1973,logistic2,0.6
-1950,1993,1982,rf2,0.77
-1950,1998,1991,rf2,0.83
-1950,1986,1978,extra2,0.74
-1950,1976,1976,decision2,0.4
-1950,1994,1973,decision1,0.52
-1950,1996,1975,extra1,0.69
-1950,1992,1988,decision1,0.58
-1950,1989,1983,logistic2,0.59
-1950,1998,1982,extra2,0.77
-1950,1978,1974,extra2,0.8
-1950,1983,1973,rf2,0.79
-1950,1979,1974,rf2,0.77
-1950,1982,1973,logistic1,0.71
-1950,1990,1977,logistic1,0.59
-1950,1977,1977,rf1,0.7
-1950,1982,1979,extra2,0.75
-1950,1995,1990,extra1,0.73
-1950,1992,1991,extra2,0.73
-1950,1997,1991,logistic2,0.74
-1950,1986,1973,extra2,0.76
-1950,1994,1983,decision1,0.47
-1950,1992,1985,decision1,0.6
-1950,1983,1983,logistic2,0.62
-1950,1992,1982,rf1,0.74
-1950,1980,1975,decision2,0.45
-1950,1991,1990,logistic1,0.71
-1950,1987,1982,extra1,0.68
-1950,1983,1980,extra1,0.72
-1950,1995,1993,decision1,0.65
-1950,1991,1978,logistic2,0.6
-1950,1986,1985,rf1,0.6
-1950,1991,1984,rf2,0.64
-1950,1992,1979,rf1,0.61
-1950,1998,1985,decision2,0.6
-1950,1984,1974,logistic2,0.64
-1950,1982,1976,extra2,0.68
-1950,1986,1975,extra2,0.77
-1950,1996,1973,decision1,0.58
-1950,1980,1978,decision2,0.54
-1950,1979,1978,rf1,0.73
-1950,1987,1973,extra2,0.74
-1950,1993,1976,extra2,0.66
-1950,1996,1978,decision1,0.59
-1950,1993,1985,rf2,0.58
-1950,1979,1974,logistic1,0.83
-1950,1986,1981,rf2,0.82
-1950,1994,1980,decision1,0.69
-1950,1978,1977,rf2,0.71
-1950,1989,1989,logistic1,0.77
-1950,1978,1974,decision2,0.43
-1950,1980,1974,extra1,0.67
-1950,1987,1979,extra1,0.66
-1950,1997,1994,decision2,0.57
-1950,1985,1984,logistic2,0.66
-1950,1988,1973,extra2,0.75
-1950,1995,1973,extra1,0.68
-1950,1983,1973,logistic1,0.67
-1950,1981,1979,rf1,0.67
-1950,1985,1973,decision2,0.47
-1950,1981,1974,rf1,0.68
-1950,1994,1977,rf1,0.59
-1950,1992,1973,logistic2,0.59
-1950,1993,1989,rf1,0.78
-1950,1998,1998,rf1,0.62
-1950,1990,1974,logistic1,0.57
-1950,1993,1992,rf1,0.79
-1950,1990,1981,rf1,0.77
-1950,1999,1979,logistic1,0.68
-1950,1987,1985,extra2,0.59
-1950,1978,1977,logistic1,0.77
-1950,1998,1973,decision1,0.52
-1950,1976,1976,rf2,0.69
-1950,1977,1974,rf2,0.79
-1950,1991,1987,logistic1,0.59
-1950,1989,1974,extra1,0.65
-1950,1977,1977,extra1,0.65
-1950,1990,1973,logistic2,0.59
-1950,1985,1981,extra1,0.78
-1950,1999,1973,decision2,0.55
-1950,1990,1984,extra1,0.68
-1950,1983,1977,extra1,0.66
-1950,1988,1986,rf1,0.68
-1950,1992,1975,logistic1,0.62
-1950,1976,1976,logistic1,0.82
-1950,1981,1976,rf1,0.72
-1950,1984,1980,decision2,0.5
-1950,1996,1984,decision2,0.56
-1950,1987,1982,decision1,0.62
-1950,1990,1990,logistic2,0.72
-1950,1985,1978,extra1,0.71
-1950,1986,1981,logistic1,0.76
-1950,1993,1973,logistic2,0.59
-1950,1977,1974,logistic1,0.82
-1950,1982,1976,decision2,0.58
-1950,1987,1976,rf1,0.68
-1950,1983,1980,decision1,0.66
-1950,1995,1974,decision2,0.41
-1950,1991,1973,logistic2,0.59
-1950,1977,1977,decision1,0.56
-1950,1985,1975,extra1,0.73
-1950,1982,1982,rf2,0.79
-1950,1979,1978,extra1,0.71
-1950,1999,1976,rf2,0.72
-1950,1986,1973,decision2,0.49
-1950,1982,1979,decision2,0.51
-1950,1984,1977,extra2,0.72
-1950,1980,1975,rf2,0.76
-1950,1982,1974,rf1,0.65
-1950,1988,1976,rf2,0.67
-1950,1989,1986,decision2,0.58
-1950,1978,1974,rf2,0.79
-1950,1987,1979,decision1,0.58
-1950,1983,1983,extra2,0.75
-1950,1997,1988,decision1,0.54
-1950,1988,1983,rf1,0.69
-1950,1977,1977,logistic2,0.7
-1950,1985,1978,decision1,0.56
-1950,1985,1981,decision1,0.72
-1950,1979,1978,decision1,0.65
-1950,1984,1983,rf2,0.75
-1950,1979,1975,rf1,0.72
-1950,1991,1975,logistic2,0.65
-1950,1995,1980,rf2,0.67
-1950,1996,1981,extra2,0.84
-1950,1978,1978,rf1,0.74
-1950,1999,1973,rf1,0.77
-1950,1989,1977,extra1,0.64
-1950,1997,1985,decision1,0.63
-1950,1989,1980,extra2,0.68
-1950,1997,1975,rf2,0.74
-1950,1989,1973,extra2,0.75
-1950,1991,1984,logistic1,0.67
-1950,1984,1974,extra2,0.78
-1950,1986,1975,decision2,0.53
-1950,1981,1979,extra1,0.67
-1950,1997,1973,decision1,0.53
-1950,1988,1980,rf1,0.71
-1950,1993,1982,logistic1,0.6
-1950,1980,1978,rf2,0.77
-1950,1986,1978,decision2,0.58
-1950,1981,1974,extra1,0.72
-1950,1980,1975,logistic1,0.77
-1950,1978,1974,logistic1,0.8
-1950,1982,1982,logistic1,0.73
-1950,1987,1982,logistic2,0.71
-1950,1989,1974,decision1,0.52
-1950,1995,1990,decision1,0.6
-1950,1993,1985,logistic1,0.56
-1950,1983,1980,logistic2,0.71
-1950,1987,1973,decision2,0.52
-1950,1992,1988,logistic2,0.6
-1950,1983,1977,decision1,0.64
-1950,1995,1987,rf1,0.6
-1950,1996,1975,decision1,0.57
-1950,1994,1974,rf1,0.6
-1950,1994,1986,extra2,0.69
-1950,1995,1977,rf2,0.65
-1950,1985,1975,decision1,0.65
-1950,1977,1975,rf1,0.73
-1950,1992,1985,logistic2,0.58
-1950,1986,1985,extra1,0.53
-1950,1981,1976,extra1,0.65
-1950,1989,1983,extra2,0.77
-1950,1979,1978,logistic2,0.71
-1950,1985,1984,extra2,0.63
-1950,1980,1978,logistic1,0.77
-1950,1983,1974,rf1,0.65
-1950,1981,1974,decision1,0.67
-1950,1998,1991,logistic1,0.8
-1950,1996,1978,logistic2,0.64
-1950,1985,1973,rf2,0.79
-1950,1995,1973,decision1,0.53
-1950,1984,1983,logistic1,0.68
-1950,1977,1977,extra2,0.62
-1950,1981,1979,decision1,0.55
-1950,1992,1991,decision2,0.57
-1950,1994,1973,logistic2,0.59
-1950,1982,1974,extra1,0.62
-1950,1994,1980,logistic2,0.68
-1950,1990,1984,decision1,0.7
-1950,1990,1987,extra1,0.63
-1950,1979,1975,extra1,0.69
-1950,1981,1976,decision1,0.6
-1950,1982,1976,rf2,0.69
-1950,1988,1976,logistic1,0.63
-1950,1985,1978,logistic2,0.65
-1950,1978,1978,extra1,0.7
-1950,1984,1980,rf2,0.72
-1950,1984,1977,decision2,0.59
-1950,1998,1976,extra2,0.68
-1950,1985,1981,logistic2,0.77
-1950,1994,1983,logistic2,0.55
-1950,1977,1977,decision2,0.42
-1950,1998,1995,rf1,0.67
-1950,1994,1992,rf2,0.8
-1950,1994,1989,rf2,0.77
-1950,1982,1979,rf2,0.71
-1950,1983,1983,decision2,0.53
-1950,1987,1979,logistic2,0.64
-1950,1977,1975,extra1,0.64
-1950,1987,1985,decision2,0.55
-1950,1993,1979,rf2,0.62
-1950,1978,1978,decision1,0.51
-1950,1991,1978,extra2,0.69
-1950,1981,1979,logistic2,0.71
-1950,1979,1978,extra2,0.81
-1950,1998,1988,rf2,0.76
-1950,1981,1974,logistic2,0.66
-1950,1979,1975,decision1,0.66
-1950,1983,1977,logistic2,0.64
-1950,1988,1973,decision2,0.49
-1950,1992,1979,extra1,0.63
-1950,1978,1975,rf1,0.71
-1950,1991,1981,decision2,0.61
-1950,1991,1991,rf1,0.65
-1950,1985,1973,logistic1,0.66
-1950,1977,1975,decision1,0.64
-1950,1980,1976,rf1,0.72
-1950,1990,1978,rf1,0.69
-1950,1989,1977,decision1,0.62
-1950,1982,1974,decision1,0.54
-1950,1996,1990,rf2,0.77
-1950,1995,1993,logistic2,0.67
-1950,1988,1986,extra1,0.67
-1950,1992,1973,extra2,0.74
-1950,1990,1973,extra2,0.73
-1950,1984,1974,decision2,0.49
-1950,1982,1976,logistic1,0.64
-1950,1984,1980,logistic1,0.76
-1950,1986,1985,decision1,0.64
-1950,1981,1976,logistic2,0.68
-1950,1998,1979,decision2,0.57
-1950,1985,1975,logistic2,0.67
-1950,1983,1980,extra2,0.77
-1950,1990,1990,extra2,0.78
-1950,1997,1982,rf1,0.77
-1950,1986,1982,rf1,0.79
-1950,1978,1978,logistic2,0.75
-1950,1999,1976,logistic1,0.66
-1950,1999,1986,extra1,0.71
-1950,1977,1975,logistic2,0.73
-1950,1986,1973,rf2,0.78
-1950,1979,1978,decision2,0.52
-1950,1982,1979,logistic1,0.7
-1950,1990,1981,extra1,0.75
-1950,1996,1973,logistic2,0.59
-1950,1979,1975,logistic2,0.7
-1950,1995,1980,logistic1,0.67
-1950,1992,1982,extra1,0.71
-1950,1987,1976,extra1,0.69
-1950,1997,1975,logistic1,0.69
-1950,1990,1975,rf1,0.69
-1950,1999,1989,decision1,0.81
-1950,1986,1975,rf2,0.77
-1950,1989,1974,logistic2,0.6
-1950,1999,1983,rf1,0.72
-1950,1985,1984,decision2,0.56
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-1950,1991,1988,rf1,0.6
-1950,1995,1984,rf1,0.65
-1950,1994,1977,extra1,0.6
-1950,1980,1979,rf1,0.66
-1950,1993,1976,decision2,0.51
-1950,1993,1992,extra1,0.76
-1950,1997,1991,extra2,0.83
-1950,1993,1989,extra1,0.79
-1950,1999,1992,logistic2,0.78
-1950,1998,1985,rf2,0.62
-1950,1993,1979,logistic1,0.61
-1950,1997,1997,rf2,0.75
-1950,1978,1975,extra1,0.69
-1950,1986,1978,rf2,0.75
-1950,1990,1987,decision1,0.57
-1950,1983,1974,extra1,0.66
-1950,1996,1987,rf2,0.69
-1950,1982,1974,logistic2,0.68
-1950,1997,1994,rf2,0.75
-1950,1981,1979,extra2,0.72
-1950,1977,1977,rf2,0.71
-1950,1987,1982,extra2,0.76
-1950,1981,1974,extra2,0.79
-1950,1997,1988,logistic2,0.64
-1950,1989,1980,decision2,0.57
-1950,1996,1994,rf1,0.67
-1950,1980,1976,extra1,0.67
-1950,1977,1977,logistic1,0.81
-1950,1984,1977,rf2,0.71
-1950,1994,1989,logistic1,0.74
-1950,1984,1984,rf1,0.65
-1950,1986,1973,logistic1,0.64
-1950,1986,1975,logistic1,0.69
-1950,1978,1978,extra2,0.8
-1950,1990,1984,logistic2,0.66
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-1950,1995,1977,logistic1,0.57
-1950,1978,1975,decision1,0.67
-1950,1998,1973,logistic2,0.6
-1950,1987,1973,rf2,0.78
-1950,1986,1985,logistic2,0.57
-1950,1988,1983,extra1,0.64
-1950,1989,1977,logistic2,0.62
-1950,1989,1986,rf2,0.71
-1950,1981,1976,extra2,0.69
-1950,1998,1998,extra1,0.68
-1950,1985,1981,extra2,0.87
-1950,1988,1986,decision1,0.64
-1950,1992,1976,rf1,0.64
-1950,1983,1974,decision1,0.64
-1950,1980,1976,decision1,0.58
-1950,1981,1974,decision2,0.45
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-1950,1988,1980,extra1,0.72
-1950,1983,1983,rf2,0.74
-1950,1993,1973,extra2,0.75
-1950,1979,1978,rf2,0.81
-1950,1985,1978,extra2,0.75
-1950,1983,1977,extra2,0.69
-1950,1986,1978,logistic1,0.68
-1950,1994,1992,logistic1,0.79
-1950,1993,1983,rf1,0.64
-1950,1990,1981,decision1,0.69
-1950,1978,1978,decision2,0.52
-1950,1978,1975,logistic2,0.71
-1950,1999,1973,rf2,0.77
-1950,1996,1975,logistic2,0.7
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-1950,1995,1990,logistic2,0.71
-1950,1981,1979,decision2,0.54
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-1950,1979,1978,logistic1,0.8
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-1950,1984,1974,rf2,0.72
-1950,1989,1983,decision2,0.53
-1950,1983,1980,decision2,0.47
-1950,1987,1976,decision1,0.64
-1950,1996,1990,logistic1,0.75
-1950,1985,1975,extra2,0.8
-1950,1987,1979,extra2,0.69
-1950,1997,1979,rf1,0.67
-1950,1981,1976,decision2,0.5
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-1950,1984,1981,rf1,0.79
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-1950,1982,1974,extra2,0.8
-1950,1992,1979,decision1,0.52
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-1950,1982,1980,rf1,0.68
-1950,1994,1986,decision2,0.55
-1950,1987,1985,rf2,0.63
-1950,1993,1986,rf1,0.68
-1950,1992,1988,extra2,0.68
-1950,1994,1980,extra2,0.66
-1950,1988,1977,rf1,0.71
-1950,1984,1977,logistic1,0.65
-1950,1995,1973,logistic2,0.58
-1950,1983,1974,logistic2,0.66
-1950,1985,1974,rf1,0.66
-1950,1995,1974,rf2,0.65
-1950,1998,1988,logistic1,0.63
-1950,1998,1982,decision2,0.56
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-1950,1983,1983,logistic1,0.67
-1950,1992,1991,rf2,0.68
-1950,1987,1973,logistic1,0.62
-1950,1985,1984,rf2,0.65
-1950,1993,1992,decision1,0.67
-1950,1988,1983,decision1,0.61
-1950,1991,1978,decision2,0.56
-1950,1992,1982,decision1,0.6
-1950,1980,1979,decision1,0.56
-1950,1993,1989,decision1,0.78
-1950,1994,1977,decision1,0.56
-1950,1988,1986,logistic2,0.65
-1950,1978,1975,extra2,0.81
-1950,1986,1982,extra1,0.68
-1950,1987,1982,decision2,0.43
-1950,1988,1973,rf2,0.78
-1950,1994,1973,extra2,0.73
-1950,1990,1973,decision2,0.43
-1950,1984,1974,logistic1,0.7
-1950,1989,1986,logistic1,0.63
-1950,1989,1980,rf2,0.67
-1950,1996,1984,rf2,0.61
-1950,1999,1980,rf1,0.72
-1950,1984,1984,extra1,0.65
-1950,1981,1974,rf2,0.74
-1950,1985,1981,decision2,0.65
-1950,1978,1978,rf2,0.8
-1950,1996,1981,decision2,0.63
-1950,1986,1976,rf1,0.69
-1950,1990,1981,logistic2,0.72
-1950,1980,1976,extra2,0.72
-1950,1989,1977,extra2,0.7
-1950,1979,1979,rf1,0.67
-1950,1999,1995,extra2,0.75
-1950,1994,1974,extra1,0.58
-1950,1986,1985,extra2,0.61
-1950,1980,1979,logistic2,0.68
-1950,1981,1979,rf2,0.75
-1950,1990,1987,logistic2,0.58
-1950,1996,1978,extra2,0.72
-1950,1989,1974,extra2,0.75
-1950,1991,1981,rf2,0.77
-1950,1987,1985,logistic1,0.57
-1950,1978,1975,decision2,0.51
-1950,1985,1978,decision2,0.55
-1950,1987,1976,logistic2,0.63
-1950,1990,1978,extra1,0.68
-1950,1979,1975,rf2,0.82
-1950,1983,1977,decision2,0.57
-1950,1997,1973,logistic2,0.6
-1950,1988,1980,decision1,0.65
-1950,1978,1978,logistic1,0.84
-1950,1994,1983,extra2,0.7
-1950,1991,1991,extra1,0.67
-1950,1987,1979,decision2,0.47
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-1950,1986,1974,rf1,0.66
-1950,1981,1976,rf2,0.7
-1950,1997,1994,logistic1,0.71
-1950,1982,1974,decision2,0.49
-1950,1985,1984,logistic1,0.66
-1950,1982,1977,extra1,0.65
-1950,1995,1987,extra1,0.58
-1950,1985,1975,decision2,0.48
-1950,1993,1976,rf2,0.65
-1950,1990,1990,decision2,0.58
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-1950,1983,1980,rf2,0.71
-1950,2000,1974,decision1,0.53
-1950,1997,1997,logistic1,0.54
-1950,1996,1987,logistic1,0.58
-1950,1995,1993,extra2,0.76
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-1950,1981,1979,logistic1,0.73
-1950,1979,1975,logistic1,0.77
-1950,1986,1982,decision1,0.71
-1950,1999,1986,decision1,0.64
-1950,1980,1976,decision2,0.47
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-1950,1998,1985,logistic1,0.61
-1950,1988,1973,logistic1,0.6
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-1950,1990,1984,extra2,0.65
-1950,1988,1983,logistic2,0.6
-1950,1984,1978,rf1,0.67
-1950,1984,1984,decision1,0.64
-1950,1991,1973,decision2,0.54
-1950,1991,1988,extra1,0.66
-1950,1997,1988,extra2,0.72
-1950,1979,1979,extra1,0.64
-1950,1989,1980,logistic1,0.67
-1950,1980,1979,extra2,0.77
-1950,1984,1981,extra1,0.78
-1950,1982,1980,decision1,0.64
-1950,1985,1974,extra1,0.7
-1950,1983,1980,logistic1,0.7
-1950,1992,1991,logistic1,0.65
-1950,1999,1973,extra1,0.73
-1950,1978,1975,rf2,0.81
-1950,1997,1976,rf1,0.64
-1950,1990,1978,decision1,0.64
-1950,1982,1977,decision1,0.7
-1950,1988,1980,logistic2,0.67
-1950,1998,1976,decision2,0.58
-1950,1978,1975,logistic1,0.78
-1950,1999,1989,logistic2,0.81
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-1950,1998,1979,rf2,0.78
-1950,1999,1973,logistic1,0.62
-1950,1994,1977,logistic2,0.55
-1950,1986,1985,decision2,0.57
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-1950,1979,1979,decision1,0.62
-1950,1992,1973,decision2,0.52
-1950,1996,1984,logistic1,0.66
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-1950,1983,1974,decision2,0.54
-1950,1983,1977,rf2,0.72
-1950,1991,1991,decision1,0.65
-1950,1993,1989,logistic2,0.7
-1950,1995,1974,logistic1,0.54
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-1950,1992,1982,logistic2,0.59
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-1950,1984,1981,decision1,0.68
-1950,1999,1983,extra1,0.75
-1950,1998,1998,decision1,0.42
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-1950,1982,1980,logistic2,0.7
-1950,1995,1981,rf1,0.75
-1950,1986,1976,extra1,0.68
-1950,1986,1982,logistic2,0.77
-1950,1997,1991,decision2,0.57
-1950,1992,1979,logistic2,0.63
-1950,1981,1980,rf1,0.68
-1950,1993,1992,logistic2,0.8
-1950,1999,1998,decision2,0.49
-1950,1984,1975,rf1,0.73
-1950,1991,1975,decision2,0.55
-1950,1991,1988,decision1,0.58
-1950,1995,1984,extra1,0.67
-1950,1982,1974,rf2,0.73
-1950,1985,1978,rf2,0.73
-1950,1980,1976,rf2,0.7
-1950,1995,1990,extra2,0.8
-1950,1987,1982,rf2,0.79
-1950,1979,1979,logistic2,0.71
-1950,1991,1978,rf2,0.66
-1950,1982,1977,logistic2,0.64
-1950,1979,1976,rf1,0.71
-1950,1998,1992,rf1,0.8
-1950,1980,1979,decision2,0.47
-1950,1987,1979,rf2,0.7
-1950,1991,1985,extra1,0.55
-1950,1993,1976,logistic1,0.58
-1950,1992,1976,extra1,0.65
-1950,1994,1974,decision1,0.46
-1950,1998,1995,extra1,0.67
-1950,1993,1973,decision2,0.47
-1950,1988,1986,extra2,0.77
-1950,2000,1973,rf1,0.78
-1950,1981,1977,rf1,0.74
-1950,1985,1975,rf2,0.76
-1950,1990,1975,decision1,0.68
-1950,1995,1987,decision1,0.57
-1950,1994,1980,decision2,0.55
-1950,1998,1973,extra2,0.75
-1950,1995,1978,rf1,0.67
-1950,1985,1985,rf1,0.62
-1950,1993,1983,extra1,0.62
-1950,1980,1976,logistic1,0.66
-1950,1989,1977,decision2,0.55
-1950,1987,1976,extra2,0.7
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-1950,1990,1981,extra2,0.77
-1950,1986,1976,decision1,0.62
-1950,1985,1981,logistic1,0.74
-1950,1992,1988,decision2,0.59
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-1950,1993,1986,extra1,0.63
-1950,1997,1982,extra1,0.75
-1950,1983,1977,logistic1,0.68
-1950,1986,1974,extra1,0.69
-1950,2000,1977,logistic2,0.68
-1950,1984,1984,extra2,0.64
-1950,1996,1975,extra2,0.77
-1950,1982,1974,logistic1,0.71
-1950,1994,1990,rf1,0.69
-1950,1987,1986,rf1,0.69
-1950,1992,1985,decision2,0.55
-1950,1996,1994,extra1,0.68
-1950,1984,1978,extra1,0.71
-1950,2000,1974,logistic2,0.62
-1950,1989,1983,logistic1,0.64
-1950,1986,1979,extra1,0.67
-1950,1999,1992,extra2,0.76
-1950,1994,1993,rf1,0.64
-1950,1983,1981,rf1,0.79
-1950,1979,1976,extra1,0.65
-1950,1990,1978,logistic2,0.61
-1950,1997,1985,extra2,0.59
-1950,1987,1982,logistic1,0.72
-1950,1988,1983,extra2,0.77
-1950,1988,1977,decision1,0.59
-1950,1990,1987,extra2,0.68
-1950,1985,1978,logistic1,0.69
-1950,1989,1987,rf1,0.63
-1950,1979,1979,extra2,0.77
-1950,1995,1973,extra2,0.75
-1950,1984,1981,logistic2,0.77
-1950,1982,1980,extra2,0.74
-1950,1991,1973,rf2,0.75
-1950,1989,1981,rf1,0.76
-1950,1987,1979,logistic1,0.68
-1950,1985,1975,logistic1,0.72
-1950,1989,1974,decision2,0.46
-1950,1994,1986,rf2,0.66
-1950,2000,1989,logistic1,0.85
-1950,1983,1974,rf2,0.73
-1950,1981,1980,extra1,0.68
-1950,1988,1974,rf1,0.63
-1950,1982,1977,extra2,0.71
-1950,1979,1976,decision1,0.62
-1950,1980,1979,rf2,0.74
-1950,1990,1990,rf2,0.75
-1950,1999,1977,rf1,0.73
-1950,1986,1982,extra2,0.78
-1950,1991,1991,logistic2,0.64
-1950,1991,1988,logistic2,0.61
-1950,1979,1976,logistic2,0.67
-1950,1984,1978,decision1,0.65
-1950,1984,1975,extra1,0.71
-1950,1995,1984,decision1,0.68
-1950,1980,1977,rf1,0.73
-1950,1986,1985,rf2,0.61
-1950,1981,1977,extra1,0.66
-1950,1992,1976,decision1,0.6
-1950,1981,1980,decision1,0.62
-1950,1986,1976,logistic2,0.67
-1950,1980,1979,logistic1,0.74
-1950,1990,1973,logistic1,0.62
-1950,1994,1983,decision2,0.66
-1950,1996,1981,rf2,0.8
-1950,1983,1974,logistic1,0.7
-1950,1999,1973,decision1,0.68
-1950,1988,1980,extra2,0.66
-1950,1990,1984,decision2,0.67
-1950,1991,1978,logistic1,0.59
-1950,1986,1974,decision1,0.65
-1950,1986,1979,decision1,0.57
-1950,1992,1982,extra2,0.71
-1950,1999,1986,logistic2,0.7
-1950,1987,1974,extra1,0.68
-1950,1990,1975,logistic2,0.65
-1950,1994,1977,extra2,0.64
-1950,1981,1977,decision1,0.6
-1950,1998,1982,rf2,0.8
-1950,1997,1979,extra1,0.68
-1950,1979,1979,decision2,0.51
-1950,1982,1980,decision2,0.46
-1950,1998,1989,rf1,0.81
-1950,1981,1980,logistic2,0.74
-1950,1983,1978,rf1,0.68
-1950,1994,1974,logistic2,0.52
-1950,1991,1985,decision1,0.61
-1950,1995,1987,logistic2,0.57
-1950,1996,1991,rf1,0.7
-1950,1982,1975,rf1,0.71
-1950,1979,1976,extra2,0.72
-1950,1984,1984,decision2,0.57
-1950,1996,1978,decision2,0.57
-1950,1984,1975,decision1,0.61
-1950,1988,1977,logistic2,0.62
-1950,1982,1977,decision2,0.45
-1950,1989,1977,rf2,0.69
-1950,1997,1982,decision1,0.67
-1950,1985,1985,extra1,0.54
-1950,1986,1985,logistic1,0.58
-1950,1997,1973,extra2,0.76
-1950,1990,1990,logistic1,0.71
-1950,1983,1981,extra1,0.75
-1950,1998,1995,decision1,0.44
-1950,1985,1982,rf1,0.8
-1950,1993,1983,decision1,0.68
-1950,1984,1978,logistic2,0.65
-1950,1992,1992,rf1,0.79
-1950,1996,1994,decision1,0.64
-1950,1981,1977,logistic2,0.65
-1950,1991,1973,logistic1,0.59
-1950,1998,1979,logistic1,0.68
-1950,1992,1973,rf2,0.77
-1950,1987,1976,decision2,0.5
-1950,1984,1981,extra2,0.8
-1950,1993,1986,decision1,0.72
-1950,1984,1975,logistic2,0.68
-1950,1994,1986,logistic1,0.57
-1950,1993,1989,extra2,0.85
-1950,1985,1976,rf1,0.71
-1950,1980,1977,extra1,0.66
-1950,1997,1995,rf1,0.66
-1950,1992,1979,extra2,0.66
-1950,1987,1986,extra1,0.68
-1950,1990,1981,decision2,0.56
-1950,1991,1975,rf2,0.7
-1950,2000,1980,extra2,0.77
-1950,1986,1979,logistic2,0.67
-1950,1986,1974,logistic2,0.64
-1950,1991,1982,rf1,0.75
-1950,1983,1981,decision1,0.73
-1950,1988,1986,decision2,0.57
-1950,1993,1992,extra2,0.74
-1950,1988,1974,extra1,0.62
-1950,1985,1979,rf1,0.69
-1950,1986,1982,decision2,0.51
-1950,1979,1979,rf2,0.75
-1950,1980,1980,rf1,0.68
-1950,1987,1983,rf1,0.68
-1950,1999,1983,decision1,0.65
-1950,1999,1995,decision2,0.46
-1950,1985,1985,decision1,0.59
-1950,1979,1976,decision2,0.45
-1950,1991,1988,extra2,0.67
-1950,1982,1980,rf2,0.73
-1950,1987,1974,decision1,0.54
-1950,2000,1999,decision1,0.68
-1950,1981,1980,extra2,0.75
-1950,1980,1977,decision1,0.64
-1950,1998,1986,rf1,0.72
-1950,1995,1978,extra1,0.68
-1950,1989,1977,logistic1,0.61
-1950,1987,1980,rf1,0.7
-1950,1993,1980,extra1,0.71
-1950,1997,1991,rf2,0.79
-1950,1992,1976,logistic2,0.61
-1950,1997,1976,extra1,0.68
-1950,1989,1987,extra1,0.65
-1950,1998,1976,rf2,0.71
-1950,1996,1988,rf1,0.64
-1950,1988,1983,decision2,0.51
-1950,1996,1981,logistic1,0.74
-1950,1988,1980,decision2,0.53
-1950,1995,1993,decision2,0.6
-1950,1991,1985,logistic2,0.56
-1950,1999,1980,extra1,0.7
-1950,1998,1982,logistic1,0.75
-1950,1982,1977,rf2,0.7
-1950,1990,1987,decision2,0.54
-1950,1983,1981,logistic2,0.74
-1950,1995,1990,decision2,0.63
-1950,1989,1984,rf1,0.68
-1950,1993,1973,rf2,0.76
-1950,1992,1973,logistic1,0.59
-1950,1990,1978,extra2,0.7
-1950,1989,1974,rf2,0.7
-1950,1981,1977,extra2,0.74
-1950,1983,1975,rf1,0.74
-1950,1989,1981,extra1,0.76
-1950,1982,1980,logistic1,0.7
-1950,1995,1981,extra1,0.74
-1950,1992,1988,rf2,0.6
-1950,1996,1973,decision2,0.48
-1950,1986,1976,extra2,0.69
-1950,1984,1984,rf2,0.68
-1950,2000,1986,rf2,0.78
-1950,1998,1998,logistic2,0.54
-1950,1990,1975,extra2,0.75
-1950,1982,1975,extra1,0.69
-1950,1984,1975,extra2,0.79
-1950,2000,1996,extra1,0.62
-1950,1995,1984,logistic2,0.66
-1950,1993,1983,logistic2,0.55
-1950,1981,1980,decision2,0.55
-1950,1999,1973,logistic2,0.61
-1950,1985,1985,logistic2,0.58
-1950,1982,1977,logistic1,0.7
-1950,1984,1978,extra2,0.74
-1950,1988,1974,decision1,0.54
-1950,1991,1975,logistic1,0.64
-1950,2000,1983,decision2,0.51
-1950,1986,1986,rf1,0.72
-1950,1987,1974,logistic2,0.64
-1950,1993,1986,logistic2,0.64
-1950,1991,1991,extra2,0.71
-1950,1997,1988,decision2,0.55
-1950,1983,1978,extra1,0.73
-1950,1992,1985,rf2,0.59
-1950,1994,1980,rf2,0.68
-1950,1982,1975,decision1,0.66
-1950,1996,1994,logistic2,0.64
-1950,1988,1977,extra2,0.71
-1950,1984,1984,logistic1,0.65
-1950,1993,1977,rf1,0.57
-1950,1995,1975,rf1,0.65
-1950,1987,1986,decision1,0.62
-1950,1985,1982,extra1,0.72
-1950,1980,1980,extra1,0.65
-1950,1986,1974,extra2,0.76
-1950,1984,1981,decision2,0.55
-1950,1997,1979,decision1,0.64
-1950,1997,1982,logistic2,0.74
-1950,1990,1984,rf2,0.63
-1950,1989,1974,logistic1,0.58
-1950,1992,1982,decision2,0.59
-1950,1989,1987,decision1,0.67
-1950,1981,1977,decision2,0.54
-1950,1992,1988,logistic1,0.61
-1950,1995,1973,decision2,0.49
-1950,1985,1979,extra1,0.67
-1950,1987,1976,rf2,0.67
-1950,1988,1974,logistic2,0.62
-1950,1999,1998,rf2,0.65
-1950,1989,1981,decision1,0.7
-1950,1993,1973,logistic1,0.57
-1950,1999,1989,extra2,0.89
-1950,1994,1990,extra1,0.65
-1950,1983,1978,decision1,0.55
-1950,1983,1981,extra2,0.81
-1950,1985,1976,extra1,0.67
-1950,1986,1979,extra2,0.68
-1950,1990,1974,rf1,0.59
-1950,1982,1975,logistic2,0.68
-1950,1994,1993,extra1,0.65
-1950,1980,1980,decision1,0.6
-1950,2001,1974,logistic1,0.7
-1950,1999,1974,rf1,0.66
-1950,1996,1985,rf1,0.59
-1950,1984,1975,decision2,0.57
-1950,1993,1980,decision1,0.59
-1950,1986,1982,rf2,0.8
-1950,2000,1986,logistic1,0.72
-1950,1987,1980,extra1,0.69
-1950,1982,1978,rf1,0.74
-1950,1998,1995,logistic2,0.57
-1950,1991,1979,rf1,0.63
-1950,1983,1975,extra1,0.71
-1950,1999,1983,logistic2,0.64
-1950,2000,1993,rf1,0.7
-1950,1985,1982,decision1,0.71
-1950,1995,1978,decision1,0.58
-1950,1985,1985,extra2,0.6
-1950,1997,1991,logistic1,0.75
-1950,1980,1980,logistic2,0.74
-1950,1987,1976,logistic1,0.66
-1950,1988,1983,rf2,0.71
-1950,1982,1981,rf1,0.81
-1950,1984,1978,decision2,0.49
-1950,1988,1980,rf2,0.65
-1950,1983,1978,logistic2,0.64
-1950,1981,1980,rf2,0.72
-1950,1995,1987,extra2,0.7
-1950,1987,1986,logistic2,0.67
-1950,1996,1975,decision2,0.62
-1950,1986,1976,decision2,0.44
-1950,1988,1986,rf2,0.7
-1950,2000,1974,extra2,0.79
-1950,1987,1983,extra1,0.65
-1950,1998,1992,extra1,0.78
-1950,1998,1976,logistic1,0.67
-1950,1994,1980,logistic1,0.64
-1950,1994,1977,decision2,0.5
-1950,1985,1976,decision1,0.62
-1950,1985,1979,decision1,0.67
-1950,1990,1984,logistic1,0.67
-1950,1994,1983,rf2,0.67
-1950,1989,1978,rf1,0.68
-1950,1994,1974,extra2,0.71
-1950,1990,1981,rf2,0.79
-1950,1995,1981,decision1,0.74
-1950,1989,1984,extra1,0.68
-1950,1992,1985,logistic1,0.57
-1950,1981,1977,rf2,0.7
-1950,1991,1974,rf1,0.61
-1950,1999,1980,decision1,0.62
-1950,1997,1985,decision2,0.59
-1950,1987,1974,extra2,0.73
-1950,1991,1982,extra1,0.71
-1950,1981,1980,logistic1,0.73
-1950,1998,1973,decision2,0.52
-1950,1992,1992,extra1,0.78
-1950,1983,1975,decision1,0.68
-1950,1984,1981,rf2,0.82
-1950,1981,1977,logistic1,0.7
-1950,2000,1999,logistic2,0.57
-1950,1986,1982,logistic1,0.78
-1950,1988,1977,decision2,0.47
-1950,1988,1983,logistic1,0.62
-1950,1989,1981,logistic2,0.72
-1950,1988,1986,logistic1,0.65
-1950,1986,1986,extra1,0.7
-1950,1989,1987,logistic2,0.61
-1950,1999,1998,logistic1,0.53
-1950,1990,1975,decision2,0.53
-1950,1990,1987,rf2,0.66
-1950,1985,1979,logistic2,0.67
-1950,1991,1985,extra2,0.54
-1950,2000,1973,extra1,0.74
-1950,2000,1977,extra2,0.71
-1950,1992,1979,decision2,0.51
-1950,1994,1990,decision1,0.64
-1950,1997,1976,decision1,0.66
-1950,1983,1981,decision2,0.59
-1950,1992,1976,extra2,0.67
-1950,1993,1989,decision2,0.71
-1950,1985,1982,logistic2,0.75
-1950,1988,1980,logistic1,0.68
-1950,1993,1983,extra2,0.7
-1950,1985,1976,logistic2,0.66
-1950,1986,1974,decision2,0.49
-1950,1984,1981,logistic1,0.75
-1950,1990,1978,decision2,0.59
-1950,2000,1996,decision1,0.49
-1950,2001,1984,decision1,0.69
-1950,1994,1987,rf1,0.61
-1950,1987,1983,decision1,0.62
-1950,1990,1981,logistic1,0.69
-1950,1996,1978,rf2,0.72
-1950,1983,1975,logistic2,0.69
-1950,1999,1992,decision2,0.77
-1950,1989,1984,decision1,0.69
-1950,1993,1992,decision2,0.72
-1950,1988,1974,extra2,0.77
-1950,1984,1975,rf2,0.78
-1950,1991,1976,rf1,0.65
-1950,1994,1983,logistic1,0.55
-1950,1992,1974,rf1,0.61
-1950,1982,1978,extra1,0.71
-1950,1985,1985,decision2,0.53
-1950,1984,1978,rf2,0.78
-1950,1983,1978,extra2,0.76
-1950,1995,1990,rf2,0.78
-1950,1995,1984,extra2,0.63
-1950,1991,1982,decision1,0.69
-1950,1987,1986,extra2,0.8
-1950,1997,1979,logistic2,0.65
-1950,1990,1987,logistic1,0.57
-1950,1993,1986,extra2,0.68
-1950,1986,1979,decision2,0.57
-1950,1982,1981,extra1,0.77
-1950,1986,1986,decision1,0.62
-1950,1999,1986,extra2,0.76
-1950,1999,1977,extra1,0.68
-1950,1987,1980,decision1,0.58
-1950,1981,1981,rf1,0.77
-1950,1982,1978,decision1,0.64
-1950,1984,1975,logistic1,0.7
-1950,1989,1975,rf1,0.68
-1950,1996,1991,extra1,0.74
-1950,1995,1993,rf2,0.69
-1950,1995,1978,logistic2,0.63
-1950,1998,1989,extra1,0.84
-1950,1992,1982,rf2,0.75
-1950,1987,1983,logistic2,0.62
-1950,1993,1980,logistic2,0.68
-1950,1996,1988,extra1,0.7
-1950,1984,1978,logistic1,0.7
-1950,2000,1990,rf1,0.78
-1950,1994,1990,logistic2,0.68
-1950,1985,1982,extra2,0.78
-1950,1990,1974,extra1,0.66
-1950,1999,1973,extra2,0.76
-1950,1997,1973,decision2,0.39
-1950,1985,1979,extra2,0.65
-1950,1996,1973,rf2,0.79
-1950,2001,1973,rf1,0.8
-1950,1989,1984,logistic2,0.68
-1950,1983,1975,extra2,0.76
-1950,1986,1976,rf2,0.69
-1950,1991,1991,decision2,0.52
-1950,1987,1977,rf1,0.73
-1950,1999,1980,logistic2,0.71
-1950,1982,1981,decision1,0.63
-1950,1997,1995,extra1,0.64
-1950,1995,1981,logistic2,0.71
-1950,1985,1976,extra2,0.71
-1950,1982,1978,logistic2,0.63
-1950,1989,1978,extra1,0.7
-1950,1987,1980,logistic2,0.69
-1950,1995,1990,logistic1,0.69
-1950,1983,1981,rf2,0.84
-1950,1996,1982,rf1,0.75
-1950,1986,1986,logistic2,0.69
-1950,1997,1988,rf2,0.73
-1950,1991,1988,decision2,0.61
-1950,1996,1978,logistic1,0.67
-1950,1993,1977,extra1,0.59
-1950,1983,1978,decision2,0.54
-1950,1986,1983,rf1,0.69
-1950,2001,1987,logistic2,0.61
-1950,1998,1992,decision1,0.83
-1950,1999,1995,rf2,0.74
-1950,1987,1974,decision2,0.37
-1950,1998,1998,extra2,0.6
-1950,1991,1979,extra1,0.61
-1950,1989,1981,extra2,0.75
-1950,1994,1993,decision1,0.6
-1950,1998,1980,rf1,0.7
-1950,2001,1981,extra1,0.78
-1950,1988,1984,rf1,0.64
-1950,1984,1982,rf1,0.81
-1950,1988,1977,rf2,0.71
-1950,1990,1985,rf1,0.59
-1950,1988,1974,decision2,0.44
-1950,1992,1989,rf1,0.76
-1950,1991,1974,extra1,0.61
-1950,1992,1992,decision1,0.76
-1950,1996,1994,extra2,0.8
-1950,1988,1987,rf1,0.66
-1950,1986,1974,rf2,0.75
-1950,1998,1983,rf1,0.69
-1950,1997,1982,extra2,0.77
-1950,1998,1986,extra1,0.73
-1950,1983,1981,logistic1,0.76
-1950,1985,1985,rf2,0.63
-1950,1982,1981,logistic2,0.76
-1950,1993,1974,rf1,0.58
-1950,1989,1987,extra2,0.7
-1950,1995,1975,extra1,0.67
-1950,1991,1985,decision2,0.6
-1950,1992,1982,logistic1,0.6
-1950,2000,1973,decision1,0.59
-1950,2001,1978,rf1,0.75
-1950,1987,1986,decision2,0.61
-1950,1986,1976,logistic1,0.65
-1950,1988,1981,rf1,0.78
-1950,1992,1986,rf1,0.66
-1950,1984,1976,rf1,0.7
-1950,1995,1973,rf2,0.77
-1950,1990,1982,rf1,0.77
-1950,1997,1976,logistic2,0.64
-1950,1990,1975,rf2,0.69
-1950,1986,1979,rf2,0.73
-1950,2000,1983,rf2,0.81
-1950,1983,1975,decision2,0.55
-1950,1991,1982,logistic2,0.68
-1950,1985,1982,decision2,0.62
-1950,1995,1993,logistic1,0.66
-1950,1982,1978,extra2,0.74
-1950,1993,1986,decision2,0.61
-1950,2001,1984,logistic2,0.67
-1950,1985,1979,decision2,0.47
-1950,1990,1978,rf2,0.66
-1950,1995,1987,decision2,0.55
-1950,1985,1985,logistic1,0.61
-1950,1987,1983,extra2,0.77
-1950,2000,1996,logistic2,0.56
-1950,1996,1973,logistic1,0.59
-1950,1994,1977,rf2,0.66
-1950,1997,1985,rf2,0.63
-1950,1989,1978,decision1,0.57
-1950,1992,1979,rf2,0.63
-1950,1994,1974,decision2,0.57
-1950,1996,1975,rf2,0.75
-1950,1985,1976,decision2,0.51
-1950,1996,1988,decision1,0.54
-1950,1984,1979,rf1,0.66
-1950,1988,1977,logistic1,0.57
-1950,1982,1981,extra2,0.81
-1950,1996,1985,extra1,0.58
-1950,1990,1988,rf1,0.58
-1950,1986,1986,extra2,0.79
-1950,1993,1989,rf2,0.77
-1950,1993,1980,extra2,0.68
-1950,1983,1978,rf2,0.71
-1950,1989,1975,extra1,0.71
-1950,1998,1977,rf1,0.69
-1950,1996,1991,decision1,0.64
-1950,1997,1995,decision1,0.43
-1950,1993,1983,decision2,0.49
-1950,1984,1976,extra1,0.64
-1950,1993,1992,rf2,0.8
-1950,1991,1979,decision1,0.64
-1950,1994,1981,rf1,0.77
-1950,1992,1976,decision2,0.43
-1950,1986,1979,logistic1,0.66
-1950,1991,1974,decision1,0.51
-1950,1984,1982,extra1,0.72
-1950,1998,1995,extra2,0.71
-1950,1998,1989,decision1,0.76
-1950,1993,1977,decision1,0.48
-1950,1987,1980,extra2,0.72
-1950,1999,1983,extra2,0.8
-1950,1989,1984,extra2,0.68
-1950,1983,1978,logistic1,0.68
-1950,1991,1976,extra1,0.65
-1950,1997,1988,logistic1,0.63
-1950,1983,1982,rf1,0.82
-1950,1990,1975,logistic1,0.66
-1950,1992,1992,logistic2,0.8
-1950,1988,1974,rf2,0.72
-1950,2001,1990,extra2,0.78
-1950,1994,1987,extra1,0.56
-1950,1987,1974,rf2,0.73
-1950,1986,1983,extra1,0.65
-1950,1988,1987,extra1,0.65
-1950,1987,1977,extra1,0.63
-1950,1992,1974,extra1,0.64
-1950,2000,1999,extra2,0.69
-1950,1982,1981,decision2,0.54
-1950,1987,1986,rf2,0.73
-1950,1990,1978,logistic1,0.6
-1950,1989,1978,logistic2,0.61
-1950,1999,1989,decision2,0.71
-1950,1994,1993,logistic2,0.65
-1950,2001,1981,decision1,0.73
-1950,1998,1973,rf2,0.76
-1950,2001,1999,logistic1,0.55
-1950,1983,1975,rf2,0.76
-1950,1999,1977,decision1,0.57
-1950,1997,1992,rf1,0.81
-1950,1990,1985,extra1,0.61
-1950,1999,1995,logistic1,0.63
-1950,1988,1984,extra1,0.63
-1950,1984,1976,decision1,0.64
-1950,1985,1982,rf2,0.8
-1950,1991,1988,rf2,0.61
-1950,1995,1978,extra2,0.72
-1950,1999,1974,extra1,0.7
-1950,1992,1989,extra1,0.8
-1950,1989,1981,decision2,0.65
-1950,1984,1982,decision1,0.71
-1950,1985,1979,rf2,0.72
-1950,1995,1975,decision1,0.64
-1950,1989,1975,decision1,0.54
-1950,1994,1990,extra2,0.75
-1950,1986,1986,decision2,0.6
-1950,1992,1979,logistic1,0.61
-1950,2000,1993,extra1,0.76
-1950,1997,1985,logistic1,0.55
-1950,1986,1983,decision1,0.7
-1950,1994,1977,logistic1,0.53
-1950,1986,1977,rf1,0.71
-1950,1994,1984,rf1,0.67
-1950,1995,1973,logistic1,0.61
-1950,1991,1982,extra2,0.75
-1950,1998,1986,decision1,0.65
-1950,1991,1991,rf2,0.67
-1950,1985,1976,rf2,0.69
-1950,1983,1975,logistic1,0.69
-1950,1997,1979,extra2,0.73
-1950,1993,1974,extra1,0.62
-1950,1988,1974,logistic1,0.59
-1950,1993,1992,logistic1,0.8
-1950,1987,1974,logistic1,0.61
-1950,1993,1989,logistic1,0.72
-1950,1987,1986,logistic1,0.71
-1950,1995,1981,extra2,0.75
-1950,2000,1980,decision2,0.53
-1950,1988,1981,extra1,0.74
-1950,1998,1992,logistic2,0.79
-1950,1988,1987,decision1,0.71
-1950,1991,1979,logistic2,0.62
-1950,1999,1992,rf2,0.75
-1950,1996,1975,logistic1,0.68
-1950,1991,1976,decision1,0.57
-1950,1995,1991,rf1,0.69
-1950,1985,1982,logistic1,0.76
-1950,1984,1979,extra1,0.68
-1950,1987,1983,decision2,0.48
-1950,1984,1982,logistic2,0.77
-1950,2001,1975,rf1,0.75
-1950,1983,1979,rf1,0.64
-1950,1991,1974,logistic2,0.6
-1950,2000,1973,logistic2,0.59
-1950,1992,1974,decision1,0.57
-1950,2000,1987,rf1,0.69
-1950,1984,1976,logistic2,0.67
-1950,1989,1987,decision2,0.56
-1950,1999,1980,extra2,0.75
-1950,1987,1980,decision2,0.52
-1950,2000,1983,logistic1,0.69
-1950,1996,1985,decision1,0.61
-1950,1987,1977,decision1,0.55
-1950,1997,1973,rf2,0.77
-1950,2001,1996,rf2,0.73
-1950,1988,1978,rf1,0.67
-1950,1983,1982,extra1,0.71
-1950,1985,1979,logistic1,0.69
-1950,1991,1985,rf2,0.6
-1950,1990,1988,extra1,0.64
-1950,1986,1980,rf1,0.7
-1950,1992,1986,extra1,0.66
-1950,1994,1987,decision1,0.56
-1950,1988,1984,decision1,0.63
-1950,1999,1999,rf1,0.69
-1950,2000,1974,decision2,0.55
-1950,1993,1977,logistic2,0.54
-1950,1989,1984,decision2,0.56
-1950,1993,1986,rf2,0.66
-1950,1985,1976,logistic1,0.67
-1950,1996,1988,logistic2,0.63
-1950,2001,1973,extra1,0.74
-1950,2001,1993,decision2,0.62
-1950,1991,1991,logistic1,0.64
-1950,1998,1973,logistic1,0.61
-1950,1993,1983,rf2,0.64
-1950,1989,1975,logistic2,0.68
-1950,1996,1991,logistic2,0.71
-1950,1990,1985,decision1,0.56
-1950,1986,1983,logistic2,0.62
-1950,1997,1995,logistic2,0.61
-1950,1991,1988,logistic1,0.6
-1950,1992,1983,rf1,0.65
-1950,1983,1982,decision1,0.63
-1950,1992,1989,decision1,0.75
-1950,1988,1981,decision1,0.74
-1950,1990,1982,extra1,0.72
-1950,1988,1987,logistic2,0.63
-1950,1984,1982,extra2,0.78
-1950,1993,1974,decision1,0.64
-1950,1984,1979,decision1,0.64
-1950,1995,1975,logistic2,0.67
-1950,1993,1980,decision2,0.56
-1950,2000,1977,decision2,0.56
-1950,1995,1984,decision2,0.58
-1950,1996,1982,extra1,0.71
-1950,1992,1976,rf2,0.66
-1950,1988,1984,logistic2,0.67
-1950,1989,1978,extra2,0.69
-1950,1989,1981,rf2,0.8
-1950,1994,1974,rf2,0.63
-1950,1991,1985,logistic1,0.56
-1950,1996,1994,decision2,0.61
-1950,1987,1977,logistic2,0.62
-1950,1998,1989,logistic2,0.81
-1950,1986,1977,extra1,0.66
-1950,1983,1976,rf1,0.71
-1950,1992,1974,logistic2,0.57
-1950,1986,1986,rf2,0.75
-1950,2000,1990,extra1,0.79
-1950,1995,1994,rf1,0.63
-1950,1995,1987,rf2,0.67
-1950,1994,1981,extra1,0.73
-1950,1991,1976,logistic2,0.6
-1950,1985,1983,rf1,0.69
-1950,1993,1986,logistic1,0.58
-1950,1996,1979,rf1,0.65
-1950,1983,1979,extra1,0.66
-1950,1987,1983,rf2,0.71
-1950,1997,1976,extra2,0.67
-1950,1992,1992,extra2,0.75
-1950,1990,1976,rf1,0.66
-1950,1990,1979,rf1,0.65
-1950,1984,1976,extra2,0.69
-1950,1999,1974,decision1,0.64
-1950,1984,1979,logistic2,0.66
-1950,1990,1985,logistic2,0.55
-1950,2001,1981,logistic2,0.75
-1950,1987,1987,rf1,0.67
-1950,1987,1975,rf1,0.73
-1950,1999,1992,logistic1,0.78
-1950,1991,1979,extra2,0.67
-1950,1990,1988,decision1,0.59
-1950,1997,1973,logistic1,0.58
-1950,1986,1986,logistic1,0.73
-1950,1988,1981,logistic2,0.74
-1950,1993,1983,logistic1,0.55
-1950,2001,1987,extra2,0.73
-1950,1987,1980,rf2,0.72
-1950,1992,1986,decision1,0.65
-1950,1998,1980,extra1,0.7
-1950,1988,1975,rf1,0.72
-1950,1994,1987,logistic2,0.57
-1950,1998,1983,extra1,0.75
-1950,2001,1996,logistic1,0.55
-1950,1989,1981,logistic1,0.7
-1950,1994,1993,extra2,0.73
-1950,2002,1984,logistic1,0.63
-1950,1988,1984,extra2,0.66
-1950,1986,1977,decision1,0.64
-1950,1986,1980,extra1,0.7
-1950,1985,1980,rf1,0.68
-1950,1985,1977,rf1,0.74
-1950,1988,1987,extra2,0.7
-1950,1997,1989,rf1,0.8
-1950,1991,1982,decision2,0.59
-1950,1986,1983,extra2,0.77
-1950,1998,1986,logistic2,0.66
-1950,1984,1982,decision2,0.48
-1950,1992,1976,logistic1,0.58
-1950,1989,1984,rf2,0.64
-1950,1987,1983,logistic1,0.66
-1950,2002,1973,rf1,0.8
-1950,1989,1987,rf2,0.71
-1950,1984,1976,decision2,0.48
-1950,1995,1978,decision2,0.6
-1950,1991,1974,extra2,0.69
-1950,1990,1982,decision1,0.67
-1950,1992,1989,logistic2,0.71
-1950,1989,1975,extra2,0.75
-1950,1996,1974,rf1,0.62
-1950,1999,1977,logistic2,0.65
-1950,1999,1983,decision2,0.6
-1950,2000,1993,decision1,0.64
-1950,1988,1978,extra1,0.71
-1950,1993,1974,logistic2,0.52
-1950,2000,1996,extra2,0.68
-1950,1999,1986,decision2,0.53
-1950,1998,1998,decision2,0.52
-1950,1994,1981,decision1,0.64
-1950,1992,1980,rf1,0.69
-1950,2001,1973,decision1,0.48
-1950,2002,1975,extra2,0.75
-1950,1987,1977,extra2,0.73
-1950,1985,1983,extra1,0.64
-1950,1999,1989,rf2,0.86
-1950,2001,1984,extra2,0.7
-1950,1987,1980,logistic1,0.7
-1950,1984,1979,extra2,0.68
-1950,1998,1977,extra1,0.68
-1950,1997,1982,decision2,0.64
-1950,1997,1992,extra1,0.76
-1950,1995,1974,rf1,0.58
-1950,1986,1980,decision1,0.65
-1950,1994,1990,decision2,0.63
-1950,1993,1977,extra2,0.6
-1950,1994,1974,logistic1,0.52
-1950,1996,1982,decision1,0.68
-1950,1993,1990,rf1,0.68
-1950,1986,1977,logistic2,0.64
-1950,1993,1993,rf1,0.65
-1950,1994,1984,extra1,0.65
-1950,1994,1978,rf1,0.66
-1950,1996,1985,logistic2,0.54
-1950,2001,1978,extra1,0.75
-1950,1997,1979,decision2,0.43
-1950,1991,1989,rf1,0.77
-1950,2002,1981,rf2,0.85
-1950,1991,1976,extra2,0.66
-1950,1995,1987,logistic1,0.56
-1950,1989,1984,logistic1,0.7
-1950,1991,1986,rf1,0.67
-1950,1990,1988,logistic2,0.63
-1950,1988,1978,decision1,0.54
-1950,1988,1984,decision2,0.58
-1950,1993,1980,rf2,0.66
-1950,1987,1987,extra1,0.64
-1950,1985,1983,decision1,0.63
-1950,1987,1975,extra1,0.71
-1950,1989,1978,decision2,0.53
-1950,1984,1982,rf2,0.8
-1950,1995,1981,decision2,0.61
-1950,1990,1985,extra2,0.58
-1950,1986,1983,decision2,0.51
-1950,2002,1978,decision2,0.6
-1950,2000,1980,rf2,0.76
-1950,1992,1983,extra1,0.62
-1950,1998,1983,decision1,0.62
-1950,1989,1987,logistic1,0.61
-1950,2002,1994,decision1,0.66
-1950,1995,1991,extra1,0.69
-1950,1985,1977,extra1,0.67
-1950,1997,1986,rf1,0.68
-1950,1988,1981,extra2,0.8
-1950,1985,1980,extra1,0.7
-1950,1996,1991,extra2,0.84
-1950,1992,1974,extra2,0.68
-1950,1996,1976,rf1,0.65
-1950,2001,1993,rf2,0.76
-1950,1986,1980,logistic2,0.7
-1950,2000,1990,decision1,0.7
-1950,1996,1994,rf2,0.71
-1950,1990,1982,logistic2,0.68
-1950,1995,1975,extra2,0.69
-1950,2000,1974,rf2,0.8
-1950,1996,1988,extra2,0.69
-1950,1990,1979,extra1,0.66
-1950,1992,1986,logistic2,0.59
-1950,1990,1976,extra1,0.69
-1950,1998,1992,extra2,0.76
-1950,1988,1987,decision2,0.56
-1950,1995,1984,rf2,0.61
-1950,1998,1980,decision1,0.6
-1950,1991,1979,decision2,0.51
-1950,1999,1996,rf1,0.69
-1950,1998,1974,rf1,0.63
-1950,1997,1995,extra2,0.69
-1950,1993,1987,rf1,0.59
-1950,1987,1977,decision2,0.49
-1950,1989,1982,rf1,0.77
-1950,1984,1979,decision2,0.5
-1950,1984,1982,logistic1,0.76
-1950,1998,1995,decision2,0.45
-1950,1988,1978,logistic2,0.64
-1950,1988,1975,extra1,0.71
-1950,1987,1975,decision1,0.66
-1950,1987,1984,rf1,0.66
-1950,2000,1977,rf2,0.79
-1950,1993,1984,rf1,0.68
-1950,1992,1992,decision2,0.73
-1950,1985,1983,logistic2,0.65
-1950,1987,1987,decision1,0.59
-1950,1986,1977,extra2,0.73
-1950,1991,1982,rf2,0.75
-1950,1993,1974,extra2,0.68
-1950,2001,1990,decision2,0.64
-1950,1995,1994,extra1,0.64
-1950,2000,1999,decision2,0.6
-1950,1999,1999,extra1,0.7
-1950,1985,1980,decision1,0.62
-1950,2001,2000,rf1,0.76
-1950,2000,1973,extra2,0.75
-1950,2000,1987,extra1,0.7
-1950,1994,1984,decision1,0.67
-1950,1998,1989,extra2,0.89
-1950,1985,1977,decision1,0.59
-1950,1987,1981,rf1,0.82
-1950,1992,1989,extra2,0.86
-1950,1997,1976,decision2,0.53
-1950,1999,1989,logistic1,0.84
-1950,1994,1987,extra2,0.67
-1950,2000,1984,rf1,0.65
-1950,2002,1991,extra1,0.86
-1950,1989,1975,decision2,0.52
-1950,1986,1980,extra2,0.73
-1950,1991,1974,decision2,0.47
-1950,1988,1984,rf2,0.63
-1950,1995,1978,rf2,0.64
-1950,1996,1982,logistic2,0.74
-1950,2002,1988,rf1,0.69
-1950,1992,1977,rf1,0.58
-1950,1999,1980,decision2,0.52
-1950,1994,1981,logistic2,0.71
-1950,2001,1973,logistic2,0.61
-1950,1988,1975,decision1,0.68
-1950,2001,1975,extra1,0.76
-1950,1990,1976,decision1,0.59
-1950,1993,1980,logistic1,0.64
-1950,2001,1978,decision1,0.67
-1950,1997,1992,decision1,0.82
-1950,1998,1977,decision1,0.61
-1950,1987,1975,logistic2,0.68
-1950,1986,1983,rf2,0.73
-1950,1996,1994,logistic1,0.63
-1950,1996,1979,extra1,0.64
-1950,1984,1979,rf2,0.75
-1950,1993,1993,extra1,0.62
-1950,1990,1988,extra2,0.7
-1950,2002,1997,logistic2,0.63
-1950,1994,1993,decision2,0.68
-1950,1999,1974,logistic2,0.65
-1950,1987,1987,logistic2,0.63
-1950,1989,1978,rf2,0.7
-1950,1990,1979,decision1,0.5
-1950,1988,1987,rf2,0.74
-1950,1985,1980,logistic2,0.72
-1950,1987,1977,rf2,0.72
-1950,1992,1980,extra1,0.67
-1950,1995,1974,extra1,0.63
-1950,1992,1983,decision1,0.57
-1950,2000,1974,logistic1,0.7
-1950,1995,1991,decision1,0.63
-1950,1989,1985,rf1,0.59
-1950,1989,1988,rf1,0.61
-1950,1993,1977,decision2,0.52
-1950,1998,1998,rf2,0.63
-1950,1988,1981,decision2,0.55
-1950,1990,1982,extra2,0.73
-1950,1988,1978,extra2,0.71
-1950,1999,1986,rf2,0.79
-1950,1995,1984,logistic1,0.66
-1950,1985,1983,extra2,0.76
-1950,1991,1976,decision2,0.47
-1950,1991,1989,extra1,0.78
-1950,1997,1974,rf1,0.62
-1950,1986,1980,decision2,0.54
-1950,1990,1985,decision2,0.59
-1950,1991,1982,logistic1,0.61
-1950,1986,1977,decision2,0.56
-1950,1996,1974,extra1,0.67
-1950,1994,1990,rf2,0.74
-1950,1995,1981,rf2,0.76
-1950,1988,1984,logistic1,0.66
-1950,2000,1980,logistic1,0.73
-1950,1997,1982,rf2,0.79
-1950,1991,1979,rf2,0.63
-1950,1997,1989,extra1,0.85
-1950,1987,1984,extra1,0.66
-1950,1986,1983,logistic1,0.69
-1950,1997,1979,rf2,0.7
-1950,1993,1990,extra1,0.68
-1950,1989,1982,extra1,0.7
-1950,1992,1986,extra2,0.7
-1950,2000,1977,logistic1,0.67
-1950,2002,2000,extra2,0.81
-1950,1998,1986,extra2,0.79
-1950,2001,1987,decision2,0.52
-1950,1988,1975,logistic2,0.69
-1950,1994,1984,logistic2,0.65
-1950,2000,1990,logistic2,0.77
-1950,1991,1986,extra1,0.64
-1950,1987,1977,logistic1,0.61
-1950,1998,1983,logistic2,0.63
-1950,2001,1981,extra2,0.84
-1950,1990,1976,logistic2,0.63
-1950,1994,1975,rf1,0.64
-1950,1987,1981,extra1,0.76
-1950,1996,1985,extra2,0.58
-1950,1999,1983,rf2,0.78
-1950,1999,1993,rf1,0.72
-1950,1992,1980,decision1,0.62
-1950,1989,1975,rf2,0.74
-1950,1995,1988,rf1,0.6
-1950,1995,1975,decision2,0.48
-1950,1999,1977,extra2,0.76
-1950,1995,1994,decision1,0.71
-1950,1988,1987,logistic1,0.64
-1950,1994,1978,extra1,0.68
-1950,2002,1981,logistic1,0.79
-1950,1989,1978,logistic1,0.62
-1950,2001,1993,logistic1,0.75
-1950,1992,1992,rf2,0.81
-1950,2000,1993,logistic2,0.68
-1950,2002,1985,rf1,0.63
-1950,1996,1991,decision2,0.56
-1950,1990,1979,logistic2,0.65
-1950,1993,1987,extra1,0.56
-1950,2001,1997,rf1,0.76
-1950,1987,1987,extra2,0.69
-1950,1992,1974,decision2,0.47
-1950,1992,1977,extra1,0.59
-1950,2002,1973,extra1,0.77
-1950,1993,1974,decision2,0.54
-1950,2000,1987,decision1,0.62
-1950,1993,1993,decision1,0.58
-1950,1998,1980,logistic2,0.68
-1950,1988,1978,decision2,0.58
-1950,2002,1978,rf2,0.82
-1950,1987,1984,decision1,0.67
-1950,1987,1981,decision1,0.75
-1950,1993,1984,extra1,0.66
-1950,1991,1989,decision1,0.73
-1950,1999,1999,decision1,0.47
-1950,1991,1974,rf2,0.64
-1950,1995,1978,logistic1,0.62
-1950,2002,1994,logistic2,0.77
-1950,1996,1976,extra1,0.68
-1950,2002,1991,decision1,0.63
-1950,2001,1975,decision1,0.64
-1950,1997,1995,decision2,0.42
-1950,1998,1995,rf2,0.73
-1950,1991,1979,logistic1,0.62
-1950,1989,1982,decision1,0.67
-1950,1988,1981,rf2,0.8
-1950,1994,1981,extra2,0.76
-1950,1994,1990,logistic1,0.68
-1950,1990,1988,decision2,0.59
-1950,1986,1977,rf2,0.71
-1950,1989,1975,logistic1,0.68
-1950,1992,1983,logistic2,0.57
-1950,1997,1986,extra1,0.68
-1950,1999,1986,logistic1,0.7
-1950,1990,1982,decision2,0.62
-1950,1995,1981,logistic1,0.71
-1950,1986,1984,rf1,0.66
-1950,1994,1987,decision2,0.53
-1950,1995,1991,logistic2,0.65
-1950,1986,1980,rf2,0.72
-1950,1998,1989,decision2,0.73
-1950,1989,1988,extra1,0.66
-1950,1988,1985,rf1,0.59
-1950,1997,1992,logistic2,0.79
-1950,1997,1976,rf2,0.66
-1950,1993,1990,decision1,0.65
-1950,1998,1974,extra1,0.64
-1950,2003,1979,decision1,0.53
-1950,1987,1984,logistic2,0.64
-1950,1992,1989,decision2,0.7
-1950,2003,1973,rf1,0.8
-1950,1986,1977,logistic1,0.66
-1950,1987,1987,decision2,0.51
-1950,2001,1990,rf2,0.81
-1950,1991,1986,decision1,0.52
-1950,1996,1974,decision1,0.6
-1950,1988,1975,extra2,0.75
-1950,1990,1976,extra2,0.68
-1950,1987,1981,logistic2,0.76
-1950,1996,1982,extra2,0.74
-1950,1996,1988,decision2,0.61
-1950,1992,1992,logistic1,0.8
-1950,1998,1977,logistic2,0.64
-1950,1992,1980,logistic2,0.68
-1950,1991,1976,rf2,0.64
-1950,1988,1981,logistic1,0.72
-1950,1990,1985,rf2,0.6
-1950,1999,1996,extra1,0.64
-1950,1987,1978,rf1,0.69
-1950,2003,1994,logistic1,0.76
-1950,1997,1982,logistic1,0.75
-1950,1994,1993,rf2,0.67
-1950,1994,1978,decision1,0.61
-1950,1998,1998,logistic1,0.53
-1950,2002,1975,decision2,0.57
-1950,2001,1978,logistic2,0.68
-1950,1996,1979,decision1,0.6
-1950,1997,1989,decision1,0.77
-1950,2003,1976,extra1,0.65
-1950,1989,1985,extra1,0.59
-1950,1993,1987,decision1,0.56
-1950,1992,1977,decision1,0.54
-1950,1986,1980,logistic1,0.74
-1950,1993,1981,rf1,0.76
-1950,1995,1994,logistic2,0.61
-1950,1989,1982,logistic2,0.72
-1950,2000,1996,decision2,0.5
-1950,1999,1990,rf1,0.78
-1950,2000,1999,rf2,0.75
-1950,1993,1977,rf2,0.62
-1950,1991,1983,rf1,0.65
-1950,2001,1973,extra2,0.76
-1950,1991,1989,logistic2,0.7
-1950,1997,1979,logistic1,0.65
-1950,1999,1983,logistic1,0.68
-1950,1989,1988,decision1,0.6
-1950,1988,1978,rf2,0.7
-1950,1998,1992,decision2,0.81
-1950,1994,1975,extra1,0.64
-1950,1996,1995,rf1,0.61
-1950,1990,1979,extra2,0.69
-1950,1993,1984,decision1,0.63
-1950,1994,1984,extra2,0.61
-1950,1989,1979,rf1,0.64
-1950,1988,1988,rf1,0.67
-1950,1993,1993,logistic2,0.66
-1950,1995,1975,rf2,0.71
-1950,1989,1985,decision1,0.61
-1950,1991,1976,logistic1,0.61
-1950,1999,1980,rf2,0.75
-1950,1999,1974,extra2,0.78
-1950,1996,1976,decision1,0.62
-1950,2001,2000,extra1,0.74
-1950,1997,1976,logistic1,0.64
-1950,1993,1990,logistic2,0.69
-1950,1990,1985,logistic1,0.55
-1950,1990,1988,rf2,0.65
-1950,1992,1974,rf2,0.63
-1950,1988,1978,logistic1,0.65
-1950,1987,1987,rf2,0.74
-1950,2003,1982,logistic2,0.75
-1950,1998,1995,logistic1,0.58
-1950,2002,1978,logistic1,0.73
-1950,1997,1986,decision1,0.63
-1950,1992,1986,decision2,0.6
-1950,1991,1986,logistic2,0.61
-1950,1990,1982,rf2,0.78
-1950,1994,1993,logistic1,0.67
-1950,1986,1984,extra1,0.69
-1950,2002,1973,decision1,0.58
-1950,1995,1985,rf1,0.58
-1950,1987,1981,extra2,0.83
-1950,1998,1986,decision2,0.63
-1950,1987,1978,extra1,0.69
-1950,1996,1991,rf2,0.76
-1950,2001,1984,decision2,0.63
-1950,1992,1977,logistic2,0.56
-1950,1987,1984,extra2,0.67
-1950,1989,1988,logistic2,0.63
-1950,1992,1983,extra2,0.71
-1950,2001,1990,logistic1,0.78
-1950,1989,1976,rf1,0.65
-1950,1993,1987,logistic2,0.57
-1950,1993,1977,logistic1,0.54
-1950,1993,1974,rf2,0.61
-1950,2000,1990,extra2,0.8
-1950,2003,1985,extra2,0.64
-1950,1999,1999,logistic2,0.57
-1950,1990,1976,decision2,0.48
-1950,2002,1997,extra2,0.7
-1950,1988,1985,extra1,0.6
-1950,1994,1978,logistic2,0.57
-1950,1988,1975,decision2,0.56
-1950,1996,1979,logistic2,0.65
-1950,1987,1987,logistic1,0.66
-1950,1997,1974,extra1,0.66
-1950,1989,1985,logistic2,0.55
-1950,1995,1988,extra1,0.66
-1950,1991,1980,rf1,0.69
-1950,2000,1973,decision2,0.49
-1950,1995,1975,logistic1,0.64
-1950,2001,1975,logistic2,0.74
-1950,1992,1980,extra2,0.66
-1950,2002,1988,extra1,0.72
-1950,1988,1982,rf1,0.76
-1950,2003,1991,rf2,0.85
-1950,1990,1979,decision2,0.56
-1950,1994,1975,decision1,0.62
-1950,1990,1988,logistic1,0.61
-1950,1986,1981,rf1,0.81
-1950,1990,1982,logistic1,0.66
-1950,2000,1984,extra1,0.67
-1950,1989,1982,extra2,0.75
-1950,1994,1981,decision2,0.61
-1950,1996,1974,logistic2,0.58
-1950,2000,1987,logistic2,0.62
-1950,1993,1984,logistic2,0.65
-1950,1988,1988,extra1,0.68
-1950,1997,1989,logistic2,0.8
-1950,1987,1978,decision1,0.6
-1950,1998,1983,extra2,0.77
-1950,2002,1991,logistic2,0.8
-1950,1996,1985,decision2,0.59
-1950,1992,1974,logistic1,0.54
-1950,2001,1987,rf2,0.73
-1950,2001,1997,extra1,0.7
-1950,1997,1995,rf2,0.69
-1950,1992,1989,rf2,0.8
-1950,1994,1991,rf1,0.66
-1950,1995,1991,extra2,0.79
-1950,1994,1987,rf2,0.64
-1950,2000,1978,rf1,0.74
-1950,2003,1979,logistic2,0.69
-1950,1993,1974,logistic1,0.54
-1950,1995,1979,rf1,0.63
-1950,1998,1974,decision1,0.61
-1950,1989,1988,extra2,0.7
-1950,1991,1989,extra2,0.85
-1950,2000,1993,extra2,0.76
-1950,1988,1985,decision1,0.6
-1950,1995,1982,rf1,0.74
-1950,1996,1988,rf2,0.64
-1950,2000,1999,logistic1,0.54
-1950,1987,1978,logistic2,0.64
-1950,1999,1996,decision1,0.51
-1950,1989,1979,extra1,0.67
-1950,2000,1975,rf1,0.71
-1950,1998,1980,extra2,0.75
-1950,1996,1991,logistic1,0.73
-1950,1988,1988,decision1,0.6
-1950,1996,1976,logistic2,0.62
-1950,1993,1990,extra2,0.76
-1950,1999,1980,logistic1,0.72
-1950,1993,1993,extra2,0.74
-1950,1987,1981,decision2,0.68
-1950,1999,1993,extra1,0.74
-1950,1991,1983,extra1,0.63
-1950,1997,1992,extra2,0.72
-1950,2000,1981,rf1,0.82
-1950,1991,1986,extra2,0.7
-1950,2001,1978,extra2,0.81
-1950,2004,1979,logistic1,0.69
-1950,1993,1981,extra1,0.74
-1950,1987,1984,decision2,0.53
-1950,1995,1994,extra2,0.78
-1950,1997,1986,logistic2,0.66
-1950,1988,1979,rf1,0.64
-1950,2001,2000,decision1,0.58
-1950,2002,1973,logistic2,0.59
-1950,1992,1986,rf2,0.7
-1950,1992,1989,logistic1,0.72
-1950,1990,1986,rf1,0.69
-1950,2001,1981,decision2,0.7
-1950,2002,1985,extra1,0.58
-1950,2002,2000,decision2,0.61
-1950,1989,1985,extra2,0.59
-1950,1988,1975,rf2,0.72
-1950,2001,1994,rf1,0.75
-1950,1990,1979,rf2,0.65
-1950,1997,1995,logistic1,0.52
-1950,1997,1974,decision1,0.6
-1950,1990,1976,rf2,0.66
-1950,1992,1977,extra2,0.63
-1950,1998,1989,rf2,0.87
-1950,1988,1985,logistic2,0.58
-1950,1989,1976,extra1,0.68
-1950,1999,1977,decision2,0.54
-1950,1994,1975,logistic2,0.64
-1950,1988,1988,logistic2,0.63
-1950,1998,1977,extra2,0.71
-1950,1991,1977,rf1,0.63
-1950,2001,1987,logistic1,0.7
-1950,2002,1975,rf2,0.79
-1950,1989,1982,decision2,0.63
-1950,2002,1994,extra2,0.78
-1950,1988,1982,extra1,0.72
-1950,1989,1979,decision1,0.59
-1950,2003,1976,decision1,0.65
-1950,2002,1982,rf1,0.78
-1950,1996,1979,extra2,0.71
-1950,2003,2001,extra1,0.78
-1950,1992,1983,decision2,0.57
-1950,1999,1987,rf1,0.7
-1950,1993,1987,extra2,0.67
-1950,1996,1995,extra1,0.66
-1950,1997,1983,rf1,0.71
-1950,1995,1988,decision1,0.61
-1950,1994,1984,decision2,0.71
-1950,1998,1992,rf2,0.77
-1950,1991,1983,decision1,0.58
-1950,1994,1987,logistic1,0.57
-1950,1997,1977,rf1,0.66
-1950,1990,1989,rf1,0.77
-1950,1996,1982,decision2,0.56
-1950,1990,1983,rf1,0.66
-1950,1996,1988,logistic1,0.62
-1950,1994,1978,extra2,0.69
-1950,1999,1990,extra1,0.77
-1950,1989,1988,decision2,0.58
-1950,2003,1991,logistic1,0.81
-1950,1987,1978,extra2,0.72
-1950,1995,1985,extra1,0.58
-1950,2000,1984,decision1,0.64
-1950,1990,1979,logistic1,0.65
-1950,1993,1978,rf1,0.69
-1950,1993,1984,extra2,0.61
-1950,1988,1975,logistic1,0.68
-1950,1999,1984,rf1,0.66
-1950,1994,1981,rf2,0.77
-1950,1998,1974,logistic2,0.64
-1950,1997,1980,rf1,0.7
-1950,1990,1976,logistic1,0.63
-1950,1992,1980,decision2,0.57
-1950,1989,1979,logistic2,0.63
-1950,1998,1986,rf2,0.77
-1950,1989,1976,decision1,0.6
-1950,2000,1996,rf2,0.72
-1950,1994,1994,rf1,0.61
-1950,1991,1980,extra1,0.71
-1950,1991,1989,decision2,0.69
-1950,1987,1984,rf2,0.64
-1950,1988,1982,decision1,0.7
-1950,1995,1976,rf1,0.63
-1950,1994,1991,extra1,0.7
-1950,1993,1981,decision1,0.73
-1950,1992,1986,logistic1,0.59
-1950,2001,1997,decision1,0.64
-1950,1999,1974,decision2,0.54
-1950,1991,1986,decision2,0.61
-1950,1996,1974,extra2,0.77
-1950,2002,1979,rf1,0.69
-1950,1988,1979,extra1,0.67
-1950,1992,1975,rf1,0.68
-1950,2002,1988,decision1,0.61
-1950,1993,1975,rf1,0.65
-1950,2003,1998,rf1,0.65
-1950,2003,1988,decision2,0.63
-1950,1996,1985,rf2,0.59
-1950,1988,1988,extra2,0.71
-1950,2000,1973,rf2,0.79
-1950,1990,1986,extra1,0.63
-1950,2003,1973,extra1,0.76
-1950,2003,1995,rf1,0.72
-1950,1989,1985,decision2,0.61
-1950,1997,1989,extra2,0.88
-1950,2002,1991,extra2,0.91
-1950,1993,1993,decision2,0.6
-1950,1989,1982,rf2,0.77
-1950,1999,1999,extra2,0.68
-1950,1991,1983,logistic2,0.57
-1950,1996,1995,decision1,0.48
-1950,1998,1989,logistic1,0.84
-1950,1998,1996,rf1,0.7
-1950,1999,1996,logistic2,0.53
-1950,2003,1982,extra2,0.78
-1950,1995,1982,extra1,0.72
-1950,2001,1984,rf2,0.75
-1950,1989,1976,logistic2,0.63
-1950,2004,1989,decision1,0.72
-1950,1994,1981,logistic1,0.67
-1950,1991,1980,decision1,0.63
-1950,1995,1979,extra1,0.66
-1950,1994,1975,extra2,0.69
-1950,2000,1990,decision2,0.63
-1950,2000,1987,extra2,0.7
-1950,2004,1973,rf1,0.78
-1950,1996,1976,extra2,0.68
-1950,1989,1988,rf2,0.66
-1950,2001,1975,extra2,0.72
-1950,2004,1976,rf2,0.75
-1950,1995,1991,decision2,0.56
-1950,1999,1993,decision1,0.66
-1950,2000,1978,extra1,0.71
-1950,1992,1990,rf1,0.67
-1950,1988,1976,rf1,0.68
-1950,1995,1988,logistic2,0.61
-1950,2002,1985,decision1,0.58
-1950,1991,1977,extra1,0.6
-1950,1995,1994,decision2,0.61
-1950,1990,1989,extra1,0.81
-1950,1989,1982,logistic1,0.7
-1950,1990,1986,decision1,0.7
-1950,1998,1992,logistic1,0.77
-1950,1992,1983,rf2,0.68
-1950,1995,1985,decision1,0.62
-1950,2000,1975,extra1,0.75
-1950,1993,1990,decision2,0.63
-1950,1992,1977,decision2,0.45
-1950,1993,1981,logistic2,0.7
-1950,1994,1991,decision1,0.58
-1950,1997,1974,logistic2,0.59
-1950,2001,1991,rf1,0.77
-1950,1990,1980,rf1,0.69
-1950,1999,1977,rf2,0.76
-1950,2000,1981,extra1,0.78
-1950,1997,1986,extra2,0.8
-1950,1996,1992,rf1,0.8
-1950,1989,1979,extra2,0.67
-1950,2003,1979,extra2,0.77
-1950,1993,1987,decision2,0.59
-1950,2000,1996,logistic1,0.51
-1950,2001,1973,decision2,0.5
-1950,1989,1988,logistic1,0.63
-1950,1995,1982,decision1,0.54
-1950,1990,1983,extra1,0.64
-1950,1994,1984,rf2,0.62
-1950,2004,1983,rf1,0.76
-1950,1990,1977,rf1,0.61
-1950,1999,1981,rf1,0.84
-1950,1998,1986,logistic1,0.67
-1950,2001,2000,logistic2,0.58
-1950,1991,1980,logistic2,0.7
-1950,2004,1986,extra1,0.79
-1950,1994,1978,decision2,0.62
-1950,1996,1985,logistic1,0.59
-1950,2003,1985,decision2,0.57
-1950,1997,1996,rf1,0.72
-1950,1991,1989,rf2,0.8
-1950,1997,1977,extra1,0.65
-1950,1990,1986,logistic2,0.65
-1950,1998,1983,decision2,0.55
-1950,1989,1985,rf2,0.6
-1950,1992,1980,rf2,0.68
-1950,1994,1988,rf1,0.59
-1950,2004,2004,logistic1,0.7
-1950,2003,1976,logistic2,0.64
-1950,1990,1989,decision1,0.77
-1950,1991,1986,rf2,0.69
-1950,1999,1990,decision1,0.65
-1950,2002,2000,rf2,0.76
-1950,2002,1975,logistic1,0.73
-1950,1993,1984,decision2,0.59
-1950,2000,2000,rf1,0.77
-1950,1997,1980,extra1,0.66
-1950,1992,1987,rf1,0.6
-1950,1995,1979,decision1,0.57
-1950,1989,1976,extra2,0.69
-1950,1995,1976,extra1,0.67
-1950,1991,1977,decision1,0.55
-1950,1995,1985,logistic2,0.55
-1950,1999,1987,extra1,0.68
-1950,1996,1989,rf1,0.8
-1950,1996,1982,rf2,0.78
-1950,1994,1994,extra1,0.64
-1950,1992,1983,logistic1,0.55
-1950,1993,1978,extra1,0.68
-1950,1991,1983,extra2,0.71
-1950,1996,1995,logistic2,0.6
-1950,1998,1980,decision2,0.65
-1950,1997,1983,extra1,0.73
-1950,2004,1992,logistic2,0.78
-1950,2000,1984,logistic2,0.69
-1950,2001,1981,rf2,0.85
-1950,1996,1979,decision2,0.57
-1950,1992,1975,extra1,0.7
-1950,2003,1973,decision1,0.64
-1950,1990,1983,decision1,0.6
-1950,1991,1989,logistic1,0.71
-1950,1993,1993,rf2,0.67
-1950,2003,2001,decision1,0.63
-1950,1997,1977,decision1,0.47
-1950,2000,1973,logistic1,0.65
-1950,1997,1992,decision2,0.74
-1950,2001,1984,logistic1,0.66
-1950,1990,1989,logistic2,0.72
-1950,2000,1981,decision1,0.77
-1950,1999,1974,rf2,0.81
-1950,2002,1973,extra2,0.77
-1950,1989,1989,rf1,0.78
-1950,1989,1985,logistic1,0.56
-1950,1994,1984,logistic1,0.64
-1950,2002,1988,logistic2,0.65
-1950,2000,1993,decision2,0.6
-1950,1998,1974,extra2,0.8
-1950,1993,1975,extra1,0.66
-1950,2001,1994,extra1,0.74
-1950,2002,1982,extra1,0.75
-1950,1999,1993,logistic2,0.68
-1950,1995,1994,rf2,0.63
-1950,1991,1986,logistic1,0.6
-1950,1992,1980,logistic1,0.64
-1950,2001,1988,rf1,0.68
-1950,1990,1986,extra2,0.69
-1950,1998,1977,decision2,0.58
-1950,1994,1991,logistic2,0.64
-1950,1993,1990,rf2,0.76
-1950,1992,1975,decision1,0.66
-1950,2001,1997,logistic2,0.67
-1950,1992,1977,rf2,0.65
-1950,1990,1980,extra1,0.69
-1950,1995,1982,logistic2,0.66
-1950,2004,1976,logistic1,0.67
-1950,1994,1975,decision2,0.46
-1950,2004,1980,rf1,0.71
-1950,1999,1984,extra1,0.66
-1950,1992,1990,extra1,0.67
-1950,1995,1979,logistic2,0.64
-1950,1994,1982,rf1,0.74
-1950,1999,1977,logistic1,0.65
-1950,1991,1977,logistic2,0.56
-1950,1996,1974,decision2,0.49
-1950,1995,1991,rf2,0.75
-1950,1999,1996,extra2,0.68
-1950,2002,1994,decision2,0.65
-1950,2005,1974,decision1,0.61
-1950,1991,1980,extra2,0.67
-1950,2002,1997,decision2,0.65
-1950,2003,1998,extra1,0.66
-1950,2001,1978,decision2,0.55
-1950,1994,1978,rf2,0.64
-1950,1995,1988,extra2,0.72
-1950,1993,1978,decision1,0.67
-1950,1993,1981,extra2,0.76
-1950,2000,1978,decision1,0.61
-1950,1990,1977,extra1,0.61
-1950,1996,1982,logistic1,0.73
-1950,1989,1979,decision2,0.5
-1950,2004,1989,logistic2,0.83
-1950,1993,1987,rf2,0.65
-1950,2001,1981,logistic1,0.78
-1950,2002,2000,logistic1,0.62
-1950,1990,1983,logistic2,0.57
-1950,2000,1975,decision1,0.75
-1950,1992,1977,logistic1,0.55
-1950,2003,1992,rf1,0.76
-1950,1993,1993,logistic1,0.66
-1950,2005,1977,logistic2,0.66
-1950,1989,1989,extra1,0.81
-1950,1997,1989,decision2,0.76
-1950,1999,1987,decision1,0.52
-1950,1995,1985,extra2,0.57
-1950,1997,1980,decision1,0.6
-1950,1995,1976,decision1,0.61
-1950,1997,1983,decision1,0.67
-1950,1989,1976,decision2,0.6
-1950,1990,1980,decision1,0.69
-1950,2002,1985,logistic2,0.59
-1950,1994,1994,decision1,0.58
-1950,1995,1994,logistic1,0.58
-1950,1992,1984,rf1,0.67
-1950,1998,1990,rf1,0.78
-1950,2000,1990,rf2,0.79
-1950,2002,1979,extra1,0.7
-1950,1998,1996,extra1,0.63
-1950,2004,1986,decision1,0.67
-1950,2003,1988,rf2,0.76
-1950,1996,1992,extra1,0.75
-1950,1992,1975,logistic2,0.64
-1950,1993,1990,logistic1,0.67
-1950,1996,1976,decision2,0.53
-1950,1990,1989,extra2,0.84
-1950,1993,1984,rf2,0.62
-1950,1991,1983,decision2,0.53
-1950,1993,1975,decision1,0.59
-1950,2004,2001,rf2,0.84
-1950,2004,1995,extra2,0.73
-1950,2003,1995,extra1,0.7
-1950,1990,1977,decision1,0.6
-1950,1992,1990,decision1,0.64
-1950,1997,1974,extra2,0.8
-1950,2000,1987,decision2,0.61
-1950,1999,1974,logistic1,0.66
-1950,2003,1982,decision2,0.53
-1950,1998,1983,rf2,0.79
-1950,1989,1986,rf1,0.7
-1950,1992,1987,extra1,0.57
-1950,1996,1986,rf1,0.67
-1950,2004,1983,extra1,0.77
-1950,1999,1990,logistic2,0.75
-1950,1990,1986,decision2,0.64
-1950,1991,1990,rf1,0.67
-1950,1994,1988,extra1,0.68
-1950,1998,1993,rf1,0.72
-1950,1997,1977,logistic2,0.58
-1950,1995,1991,logistic1,0.66
-1950,1999,1999,decision2,0.54
-1950,1998,1980,rf2,0.75
-1950,1997,1996,extra1,0.59
-1950,1996,1979,rf2,0.67
-1950,1993,1978,logistic2,0.57
-1950,1996,1995,extra2,0.72
-1950,1993,1987,logistic1,0.58
-1950,2001,1994,decision1,0.73
-1950,1990,1980,logistic2,0.67
-1950,1992,1981,rf1,0.78
-1950,2000,1997,rf1,0.76
-1950,2004,1973,decision2,0.51
-1950,1998,1987,rf1,0.67
-1950,2001,1973,rf2,0.77
-1950,1991,1987,rf1,0.6
-1950,1994,1978,logistic1,0.59
-1950,1994,1991,extra2,0.76
-1950,2001,2000,extra2,0.79
-1950,1994,1975,rf2,0.69
-1950,1999,1981,extra1,0.79
-1950,2001,1975,decision2,0.48
-1950,1989,1979,rf2,0.67
-1950,1991,1977,extra2,0.68
-1950,1996,1989,extra1,0.83
-1950,1993,1984,logistic1,0.64
-1950,1997,1986,decision2,0.63
-1950,2001,1991,extra1,0.86
-1950,2003,1973,logistic2,0.62
-1950,1991,1980,decision2,0.6
-1950,1997,1992,rf2,0.76
-1950,1996,1992,decision1,0.85
-1950,1990,1983,extra2,0.7
-1950,1995,1979,extra2,0.65
-1950,1994,1985,rf1,0.6
-1950,1998,1977,rf2,0.74
-1950,1992,1990,logistic2,0.69
-1950,1994,1994,logistic2,0.63
-1950,1996,1974,rf2,0.7
-1950,1995,1982,extra2,0.73
-1950,2000,1993,rf2,0.77
-1950,2002,1991,decision2,0.61
-1950,2003,1985,rf2,0.67
-1950,2002,1982,decision1,0.67
-1950,1992,1978,rf1,0.68
-1950,2004,1998,decision2,0.57
-1950,2000,1981,logistic2,0.77
-1950,2000,1975,logistic2,0.74
-1950,1993,1975,logistic2,0.65
-1950,2005,1973,rf1,0.8
-1950,1999,1984,decision1,0.68
-1950,1995,1976,logistic2,0.61
-1950,1997,1983,logistic2,0.6
-1950,2005,1989,logistic1,0.83
-1950,1995,1988,decision2,0.55
-1950,1992,1987,decision1,0.51
-1950,2004,1973,extra1,0.75
-1950,1990,1989,decision2,0.67
-1950,2000,1984,extra2,0.68
-1950,2003,2001,logistic2,0.7
-1950,2003,1998,decision1,0.52
-1950,1994,1982,extra1,0.71
-1950,2000,1990,logistic1,0.78
-1950,2000,2000,extra1,0.73
-1950,1994,1988,decision1,0.6
-1950,1999,1993,extra2,0.75
-1950,1997,1980,logistic2,0.68
-1950,2003,1976,extra2,0.73
-1950,1998,1996,decision1,0.41
-1950,2003,1979,decision2,0.6
-1950,2002,1976,rf1,0.68
-1950,2004,2001,logistic1,0.75
-1950,1991,1983,rf2,0.68
-1950,1994,1975,logistic1,0.63
-1950,2000,1974,rf1,0.7
-1950,1993,1981,decision2,0.63
-1950,2002,1979,decision1,0.54
-1950,1996,1979,logistic1,0.65
-1950,2004,1992,extra2,0.74
-1950,2005,1974,logistic2,0.68
-1950,2001,1997,extra2,0.71
-1950,2000,1978,logistic2,0.69
-1950,1998,1980,logistic1,0.71
-1950,1997,1996,decision1,0.42
-1950,1999,1987,logistic2,0.62
-1950,1992,1984,extra1,0.63
-1950,2001,1978,rf2,0.82
-1950,2001,1985,rf1,0.62
-1950,1998,1983,logistic1,0.67
-1950,1996,1983,rf1,0.64
-1950,1995,1985,decision2,0.54
-1950,1998,1974,decision2,0.49
-1950,1996,1989,decision1,0.79
-1950,2003,1988,logistic1,0.67
-1950,1997,1989,rf2,0.85
-1950,2003,1995,decision1,0.46
-1950,2004,1983,decision1,0.66
-1950,1991,1990,extra1,0.71
-1950,2002,1994,rf2,0.77
-1950,2004,1986,logistic2,0.71
-1950,1993,1991,rf1,0.65
-1950,2001,1982,rf1,0.8
-1950,2002,1988,extra2,0.8
-1950,1992,1987,logistic2,0.58
-1950,1994,1991,decision2,0.57
-1950,2004,1989,extra2,0.87
-1950,1993,1978,extra2,0.71
-1950,1996,1976,rf2,0.64
-1950,2002,1997,rf2,0.77
-1950,1991,1987,extra1,0.58
-1950,2001,1991,decision1,0.62
-1950,1995,1992,rf1,0.81
-1950,2001,1988,extra1,0.7
-1950,2000,1987,rf2,0.72
-1950,1993,1975,extra2,0.68
-1950,1997,1977,extra2,0.69
-1950,2005,2002,logistic2,0.59
-1950,2001,1973,logistic1,0.66
-1950,1994,1982,decision1,0.7
-1950,1995,1995,rf1,0.53
-1950,1991,1977,decision2,0.54
-1950,1999,1978,rf1,0.73
-1950,1993,1988,rf1,0.6
-1950,1996,1986,extra1,0.67
-1950,2005,1999,decision1,0.64
-1950,1997,1992,logistic1,0.78
-1950,1992,1981,extra1,0.75
-1950,1995,1976,extra2,0.66
-1950,1991,1980,rf2,0.66
-1950,1996,1992,logistic2,0.79
-1950,2003,1985,logistic1,0.62
-1950,1990,1989,rf2,0.81
-1950,1992,1984,decision1,0.65
-1950,2004,1977,rf1,0.72
-1950,1991,1983,logistic1,0.58
-1950,1994,1988,logistic2,0.61
-1950,1999,1996,decision2,0.5
-1950,2001,1994,logistic2,0.76
-1950,1992,1990,extra2,0.77
-1950,1998,1990,extra1,0.79
-1950,1994,1979,rf1,0.62
-1950,1993,1985,rf1,0.62
-1950,2002,1973,decision2,0.56
-1950,1991,1990,decision1,0.61
-1950,2004,1980,extra1,0.75
-1950,1999,1999,rf2,0.74
-1950,1999,1984,logistic2,0.69
-1950,1999,1981,decision1,0.73
-1950,2005,1986,rf2,0.74
-1950,1996,1995,decision2,0.42
-1950,1999,1990,extra2,0.78
-1950,1994,1985,extra1,0.58
-1950,1992,1978,extra1,0.66
-1950,1994,1994,extra2,0.69
-1950,1998,1977,logistic1,0.66
-1950,2003,1982,rf2,0.78
-1950,2000,1975,extra2,0.73
-1950,1991,1987,decision1,0.61
-1950,2002,1985,extra2,0.64
-1950,2002,1982,logistic2,0.76
-1950,2004,1973,decision1,0.55
-1950,1995,1988,rf2,0.64
-1950,1995,1979,decision2,0.51
-1950,1995,1982,decision2,0.6
-1950,1998,1993,extra1,0.76
-1950,1997,1996,logistic2,0.47
-1950,1997,1983,extra2,0.76
-1950,1992,1987,extra2,0.72
-1950,1993,1978,decision2,0.59
-1950,2002,2001,rf1,0.88
-1950,2004,1973,rf2,0.81
-1950,1997,1986,rf2,0.73
-1950,2000,1997,extra1,0.69
-1950,1998,1987,extra1,0.7
-1950,2000,1993,logistic1,0.76
-1950,2005,1977,extra2,0.75
-1950,1991,1980,logistic1,0.66
-1950,2003,1992,extra1,0.76
-1950,1997,1989,logistic1,0.82
-1950,1999,1975,rf1,0.7
-1950,1993,1981,rf2,0.76
-1950,2000,2000,decision1,0.56
-1950,2006,1974,logistic1,0.72
-1950,2004,1995,decision2,0.47
-1950,2001,2000,decision2,0.57
-1950,1998,1996,logistic2,0.5
-1950,1996,1976,logistic1,0.61
-1950,2000,1981,extra2,0.84
-1950,2005,1980,extra2,0.75
-1950,2005,1983,decision2,0.53
-1950,2005,1996,extra1,0.68
-1950,1994,1985,decision1,0.54
-1950,1994,1982,logistic2,0.6
-1950,1992,1984,logistic2,0.64
-1950,1994,1991,rf2,0.73
-1950,1992,1981,decision1,0.77
-1950,1996,1989,logistic2,0.75
-1950,1994,1976,rf1,0.63
-1950,1993,1991,extra1,0.67
-1950,1997,1980,extra2,0.71
-1950,2001,1975,rf2,0.79
-1950,2002,1991,rf2,0.85
-1950,1995,1985,rf2,0.6
-1950,2001,1978,logistic1,0.73
-1950,2002,1994,logistic1,0.76
-1950,2003,1995,logistic2,0.63
-1950,2004,1983,logistic2,0.69
-1950,1991,1990,logistic2,0.69
-1950,1991,1977,rf2,0.66
-1950,2001,1988,decision1,0.61
-1950,2000,1987,logistic1,0.69
-1950,1992,1978,decision1,0.66
-1950,1996,1980,rf1,0.68
-1950,2002,1997,logistic1,0.65
-1950,1996,1986,decision1,0.68
-1950,1998,1984,rf1,0.66
-1950,2003,1973,extra2,0.79
-1950,1997,1974,decision2,0.57
-1950,2000,1984,decision2,0.7
-1950,2005,2005,extra2,0.78
-1950,1998,1990,decision1,0.71
-1950,2003,1979,rf2,0.78
-1950,2005,1993,rf1,0.74
-1950,2003,1998,logistic2,0.56
-1950,1997,1986,logistic1,0.68
-1950,1993,1975,decision2,0.56
-1950,1998,1974,rf2,0.82
-1950,2002,1979,logistic2,0.67
-1950,1993,1981,logistic1,0.67
-1950,1995,1988,logistic1,0.62
-1950,2005,1999,logistic2,0.62
-1950,1996,1983,extra1,0.64
-1950,1996,1992,extra2,0.75
-1950,1999,1993,decision2,0.58
-1950,2001,1982,extra1,0.76
-1950,1995,1992,extra1,0.82
-1950,1998,1993,decision1,0.64
-1950,1998,1981,rf1,0.82
-1950,1999,1999,logistic1,0.54
-1950,1993,1978,rf2,0.64
-1950,1994,1991,logistic1,0.65
-1950,2000,1978,extra2,0.81
-1950,2000,1997,decision1,0.47
-1950,1991,1981,rf1,0.77
-1950,1991,1984,rf1,0.65
-1950,2004,1998,rf2,0.7
-1950,2003,2001,extra2,0.84
-1950,2003,1982,logistic1,0.77
-1950,1994,1988,extra2,0.67
-1950,1993,1988,extra1,0.65
-1950,1993,1991,decision1,0.59
-1950,2000,1991,rf1,0.79
-1950,1999,1987,extra2,0.72
-1950,1999,1981,logistic2,0.75
-1950,2001,1991,logistic2,0.81
-1950,2000,1974,extra1,0.69
-1950,1992,1981,logistic2,0.7
-1950,2004,1980,decision1,0.72
-1950,1992,1984,extra2,0.63
-1950,1994,1985,logistic2,0.58
-1950,2005,1973,extra1,0.77
-1950,2003,1976,decision2,0.55
-1950,1995,1976,decision2,0.5
-1950,1996,1995,rf2,0.66
-1950,1993,1985,extra1,0.58
-1950,2002,1976,extra1,0.65
-1950,1995,1982,rf2,0.78
-1950,1992,1990,decision2,0.65
-1950,1995,1995,extra1,0.43
-1950,2005,1974,extra2,0.78
-1950,1991,1977,logistic1,0.56
-1950,2001,1985,extra1,0.57
-1950,2005,1990,rf1,0.78
-1950,1994,1979,extra1,0.62
-1950,1995,1979,rf2,0.66
-1950,2006,1973,rf1,0.78
-1950,2005,1986,logistic1,0.75
-1950,1992,1987,decision2,0.57
-1950,2001,1975,logistic1,0.74
-1950,1999,1984,extra2,0.7
-1950,2004,2002,rf1,0.67
-1950,1995,1985,logistic1,0.56
-1950,1999,1996,rf2,0.72
-1950,2003,1989,rf1,0.84
-1950,2004,1986,extra2,0.74
-1950,1997,1996,extra2,0.63
-1950,1998,1996,extra2,0.64
-1950,1997,1993,rf1,0.67
-1950,1992,1978,logistic2,0.57
-1950,1993,1988,decision1,0.6
-1950,1993,1978,logistic1,0.58
-1950,1995,1992,decision1,0.8
-1950,1994,1982,extra2,0.69
-1950,1997,1977,decision2,0.6
-1950,1994,1994,decision2,0.61
-1950,2000,2000,logistic2,0.59
-1950,1996,1977,rf1,0.64
-1950,1996,1986,logistic2,0.66
-1950,2001,1994,extra2,0.78
-1950,1999,1978,extra1,0.75
-1950,1998,1974,logistic1,0.63
-1950,2004,1992,decision2,0.75
-1950,1998,1978,rf1,0.69
-1950,1993,1975,rf2,0.71
-1950,1993,1991,logistic2,0.64
-1950,1994,1976,extra1,0.68
-1950,2004,1973,logistic1,0.67
-1950,1998,1987,decision1,0.57
-1950,2001,2000,rf2,0.74
-1950,1997,1990,rf1,0.77
-1950,1996,1983,decision1,0.65
-1950,1998,1984,extra1,0.68
-1950,1995,1995,decision1,0.42
-1950,1998,1990,logistic2,0.77
-1950,2006,1984,decision1,0.61
-1950,2001,1974,rf1,0.71
-1950,2003,1992,decision1,0.69
-1950,1992,1981,extra2,0.76
-1950,2005,1996,decision1,0.48
-1950,2002,1982,extra2,0.78
-1950,1994,1979,decision1,0.59
-1950,1996,1989,extra2,0.85
-1950,2004,1977,extra1,0.68
-1950,2002,1988,decision2,0.57
-1950,2002,1973,rf2,0.8
-1950,1993,1982,rf1,0.73
-1950,2003,1986,rf1,0.74
-1950,2003,1995,extra2,0.69
-1950,2004,1998,logistic1,0.58
-1950,2004,1973,logistic2,0.63
-1950,2005,2002,extra2,0.7
-1950,1998,1993,logistic2,0.7
-1950,2004,1995,rf2,0.79
-1950,2001,1982,decision1,0.71
-1950,2002,1991,logistic1,0.81
-1950,2006,1978,rf1,0.79
-1950,1995,1982,logistic1,0.65
-1950,2001,1988,logistic2,0.67
-1950,1991,1984,extra1,0.65
-1950,2005,1983,rf2,0.83
-1950,1999,1975,extra1,0.74
-1950,1997,1980,decision2,0.55
-1950,1992,1990,rf2,0.74
-1950,1996,1995,logistic1,0.56
-1950,1995,1979,logistic1,0.64
-1950,2004,1983,extra2,0.81
-1950,2003,1979,logistic1,0.7
-1950,1997,1983,decision2,0.65
-1950,1997,1974,rf2,0.76
-1950,1995,1989,rf1,0.79
-1950,1993,1988,logistic2,0.6
-1950,1999,1993,rf2,0.75
-1950,2004,1989,decision2,0.73
-1950,1993,1985,decision1,0.62
-1950,1994,1985,extra2,0.58
-1950,2002,2001,extra1,0.77
-1950,1996,1992,decision2,0.77
-1950,2002,1985,decision2,0.58
-1950,1994,1992,rf1,0.79
-1950,1999,1990,decision2,0.66
-1950,2000,1994,rf1,0.76
-1950,2001,1997,decision2,0.62
-1950,2005,1973,decision1,0.61
-1950,1996,1980,extra1,0.67
-1950,1995,1976,rf2,0.65
-1950,2006,1981,extra1,0.81
-1950,2000,1975,decision2,0.49
-1950,1999,1981,extra2,0.86
-1950,1992,1987,rf2,0.66
-1950,1992,1978,extra2,0.68
-1950,1993,1975,logistic1,0.62
-1950,1997,1987,rf1,0.65
-1950,2000,1997,logistic2,0.63
-1950,1999,1996,logistic1,0.49
-1950,2002,1976,decision1,0.62
-1950,2002,1995,rf1,0.71
-1950,1992,1981,decision2,0.7
-1950,1994,1982,decision2,0.6
-1950,2000,1981,decision2,0.65
-1950,2000,1984,rf2,0.73
-1950,2004,1980,logistic2,0.71
-1950,1995,1992,logistic2,0.79
-1950,1993,1979,rf1,0.61
-1950,1992,1984,decision2,0.56
-1950,2001,1991,extra2,0.9
-1950,1994,1988,decision2,0.58
-1950,1999,1978,decision1,0.66
-1950,1994,1994,rf2,0.62
-1950,2001,1979,rf1,0.69
-1950,2000,1988,rf1,0.67
-1950,1994,1976,decision1,0.54
-1950,1996,1983,logistic2,0.59
-1950,2001,1985,decision1,0.59
-1950,2002,1979,extra2,0.78
-1950,2005,1999,extra2,0.71
-1950,1997,1977,rf2,0.69
-1950,1998,1981,extra1,0.8
-1950,1999,1987,decision2,0.57
-1950,1995,1986,rf1,0.67
-1950,1998,1996,decision2,0.51
-1950,1998,1984,decision1,0.66
-1950,2003,1998,extra2,0.62
-1950,2002,1973,logistic1,0.65
-1950,1996,1986,extra2,0.79
-1950,1993,1991,extra2,0.75
-1950,1992,1990,logistic1,0.69
-1950,1997,1993,extra1,0.76
-1950,1994,1979,logistic2,0.63
-1950,1992,1987,logistic1,0.59
-1950,2000,1991,extra1,0.85
-1950,2002,1998,rf1,0.6
-1950,2005,1977,decision2,0.59
-1950,1997,1996,decision2,0.51
-1950,1995,1995,logistic2,0.48
-1950,2006,1987,logistic2,0.67
-1950,2000,1978,decision2,0.61
-1950,2001,2000,logistic1,0.63
-1950,2006,1990,extra2,0.79
-1950,1996,1980,decision1,0.62
-1950,1998,1987,logistic2,0.62
-1950,1999,1975,decision1,0.73
-1950,1998,1975,rf1,0.7
-1950,2005,1990,extra1,0.75
-1950,1996,1977,extra1,0.61
-1950,2005,1996,logistic2,0.6
-1950,2005,1980,decision2,0.58
-1950,2006,1975,rf1,0.76
-1950,2001,1976,rf1,0.67
-1950,2005,1993,extra1,0.75
-1950,2003,1983,rf1,0.74
-1950,1997,1974,logistic1,0.59
-1950,2003,1992,logistic2,0.79
-1950,1994,1994,logistic1,0.6
-1950,1995,1976,logistic1,0.6
-1950,1993,1988,extra2,0.69
-1950,1993,1985,logistic2,0.58
-1950,2003,1973,decision2,0.44
-1950,2003,1976,rf2,0.73
-1950,1999,1993,logistic1,0.76
-1950,2004,1995,logistic1,0.68
-1950,2005,1983,logistic1,0.72
-1950,1998,1993,extra2,0.75
-1950,2006,1984,logistic2,0.68
-1950,2001,1982,logistic2,0.76
-1950,1994,1976,logistic2,0.58
-1950,2003,1989,extra1,0.83
-1950,1993,1982,extra1,0.68
-1950,1998,1990,extra2,0.79
-1950,1997,1977,logistic1,0.64
-1950,2000,1984,logistic1,0.65
-1950,1995,1983,rf1,0.63
-1950,1997,1990,extra1,0.8
-1950,2000,2000,extra2,0.78
-1950,2006,1999,logistic1,0.61
-1950,1999,1978,logistic2,0.66
-1950,1996,1992,rf2,0.75
-1950,1996,1980,logistic2,0.69
-1950,1992,1984,rf2,0.63
-1950,2004,1992,rf2,0.75
-1950,2006,1996,rf2,0.73
-1950,1998,1984,logistic2,0.69
-1950,2004,2002,extra1,0.64
-1950,2005,1973,logistic2,0.64
-1950,1995,1980,rf1,0.69
-1950,1998,1978,extra1,0.7
-1950,1997,1980,rf2,0.69
-1950,2003,2001,decision2,0.62
-1950,1994,1992,extra1,0.77
-1950,1995,1992,extra2,0.77
-1950,2004,1977,decision1,0.6
-1950,1995,1989,extra1,0.8
-1950,1999,1984,decision2,0.67
-1950,1993,1979,extra1,0.62
-1950,1996,1996,rf1,0.61
-1950,1994,1982,rf2,0.78
-1950,1997,1983,rf2,0.72
-1950,1993,1991,decision2,0.58
-1950,1994,1988,rf2,0.63
-1950,1993,1982,decision1,0.75
-1950,2002,1976,logistic2,0.66
-1950,1998,1981,decision1,0.77
-1950,1996,1989,decision2,0.73
-1950,1997,1993,decision1,0.63
-1950,2005,1987,rf1,0.73
-1950,1996,1983,extra2,0.76
-1950,1996,1992,logistic1,0.76
-1950,1997,1987,extra1,0.66
-1950,2000,1991,decision1,0.64
-1950,2002,1992,rf1,0.75
-1950,2006,1973,extra1,0.77
-1950,2002,1988,rf2,0.76
-1950,1994,1985,decision2,0.54
-1950,1993,1976,rf1,0.64
-1950,2002,2001,decision1,0.75
-1950,2001,1985,logistic2,0.58
-1950,1996,1977,decision1,0.6
-1950,2004,1999,rf1,0.71
-1950,1999,1990,rf2,0.78
-1950,1999,1975,logistic2,0.74
-1950,2003,1995,decision2,0.5
-1950,1992,1991,rf1,0.64
-1950,2001,1988,extra2,0.79
-1950,2002,1985,rf2,0.67
-1950,1994,1979,extra2,0.62
-1950,1995,1995,extra2,0.53
-1950,2000,1994,extra1,0.79
-1950,2006,1978,extra1,0.76
-1950,2001,1994,decision2,0.57
-1950,2001,1997,rf2,0.77
-1950,2004,1980,extra2,0.75
-1950,2000,1975,rf2,0.8
-1950,1997,1990,decision1,0.65
-1950,1998,1996,rf2,0.7
-1950,2005,1974,decision2,0.53
-1950,2004,1992,logistic1,0.76
-1950,2004,1974,rf1,0.74
-1950,2006,1981,decision1,0.74
-1950,2003,1976,logistic1,0.67
-1950,2004,1973,extra2,0.79
-1950,1995,1986,extra1,0.63
-1950,2003,1986,extra1,0.78
-1950,2005,1993,decision1,0.61
-1950,2004,1983,decision2,0.54
-1950,2004,1986,decision2,0.6
-1950,2001,1974,extra1,0.71
-1950,2005,1990,decision1,0.73
-1950,1994,1982,logistic1,0.59
-1950,2000,1997,extra2,0.74
-1950,1994,1976,extra2,0.69
-1950,1997,1996,rf2,0.66
-1950,1998,1978,decision1,0.65
-1950,1995,1989,decision1,0.78
-1950,2002,1995,extra1,0.69
-1950,2000,1981,rf2,0.86
-1950,1997,1980,logistic1,0.7
-1950,2006,2006,extra1,0.67
-1950,2005,2005,decision2,0.63
-1950,2004,1989,rf2,0.91
-1950,1996,1986,decision2,0.64
-1950,2006,1993,decision2,0.58
-1950,1999,1987,rf2,0.72
-1950,2003,1992,extra2,0.72
-1950,1998,1987,extra2,0.72
-1950,1994,1988,logistic1,0.62
-1950,1995,1977,rf1,0.58
-1950,1997,1983,logistic1,0.62
-1950,1999,1997,rf1,0.75
-1950,1996,1980,extra2,0.71
-1950,2003,1989,decision1,0.75
-1950,1995,1992,decision2,0.72
-1950,1994,1992,decision1,0.76
-1950,1998,1975,extra1,0.71
-1950,1998,1984,extra2,0.67
-1950,2000,1978,rf2,0.81
-1950,2003,1980,rf1,0.74
-1950,2000,1988,extra1,0.72
-1950,2003,1983,extra1,0.77
-1950,1999,1990,logistic1,0.79
-1950,2005,1996,extra2,0.69
-1950,1994,1985,rf2,0.58
-1950,2002,1982,decision2,0.56
-1950,2006,1984,extra2,0.71
-1950,2001,1979,extra1,0.68
-1950,1997,1993,logistic2,0.69
-1950,1995,1983,extra1,0.61
-1950,2007,1984,logistic1,0.65
-1950,1997,1975,rf1,0.7
-1950,1997,1978,rf1,0.67
-1950,2006,1996,logistic1,0.56
-1950,2001,1991,decision2,0.6
-1950,1999,1978,extra2,0.82
-1950,1998,1993,decision2,0.6
-1950,2004,2002,decision1,0.54
-1950,1995,1980,extra1,0.71
-1950,1999,1981,decision2,0.63
-1950,2001,1982,extra2,0.79
-1950,2005,2002,decision2,0.57
-1950,2002,1998,extra1,0.65
-1950,2002,1974,rf1,0.72
-1950,2007,1994,decision1,0.56
-1950,1996,1989,rf2,0.84
-1950,2001,1974,decision1,0.61
-1950,1994,1979,decision2,0.51
-1950,2000,2000,decision2,0.53
-1950,1995,1995,decision2,0.4
-1950,1996,1983,decision2,0.56
-1950,1999,1994,rf1,0.73
-1950,2002,1988,logistic1,0.67
-1950,2005,1977,rf2,0.75
-1950,2003,1973,rf2,0.8
-1950,1998,1990,decision2,0.68
-1950,2001,2001,rf1,0.83
-1950,2006,1973,decision1,0.62
-1950,1998,1996,logistic1,0.48
-1950,1998,1981,logistic2,0.74
-1950,1997,1990,logistic2,0.76
-1950,2001,1976,extra1,0.66
-1950,1996,1996,extra1,0.46
-1950,1996,1977,logistic2,0.6
-1950,2006,1987,extra2,0.73
-1950,1997,1987,decision1,0.54
-1950,2000,1981,logistic1,0.79
-1950,2007,1973,rf1,0.76
-1950,1999,1984,rf2,0.69
-1950,2006,2003,rf1,0.85
-1950,2005,1980,rf2,0.74
-1950,1997,1996,logistic1,0.46
-1950,2000,1994,decision1,0.66
-1950,2002,1979,decision2,0.63
-1950,1995,1986,decision1,0.56
-1950,2004,1977,logistic2,0.69
-1950,2005,1999,decision2,0.58
-1950,2000,1985,rf1,0.61
-1950,1996,1993,rf1,0.68
-1950,2002,1985,logistic1,0.61
-1950,2000,1991,logistic2,0.81
-1950,2001,1997,logistic1,0.63
-1950,1995,1989,logistic2,0.73
-1950,2005,1984,rf1,0.65
-1950,2000,1988,decision1,0.66
-1950,1999,1975,extra2,0.76
-1950,2000,1975,logistic1,0.73
-1950,2006,1975,extra1,0.8
-1950,2006,1978,decision1,0.65
-1950,2002,1995,decision1,0.48
-1950,2003,1986,decision1,0.73
-1950,1994,1985,logistic1,0.56
-1950,2004,1996,rf1,0.68
-1950,2003,1998,decision2,0.53
-1950,2000,1978,logistic1,0.71
-1950,2002,2001,logistic2,0.69
-1950,2006,2006,decision1,0.64
-1950,1998,1978,logistic2,0.69
-1950,1999,1987,logistic1,0.67
-1950,2007,1981,rf2,0.8
-1950,2001,1985,extra2,0.61
-1950,1994,1976,decision2,0.56
-1950,1995,1983,decision1,0.61
-1950,1998,1975,decision1,0.65
-1950,2003,2001,rf2,0.83
-1950,2007,1975,extra2,0.81
-1950,2006,1990,decision2,0.65
-1950,2002,1976,extra2,0.71
-1950,1994,1989,rf1,0.76
-1950,1996,1989,logistic1,0.79
-1950,2005,1973,extra2,0.8
-1950,2007,1991,extra1,0.85
-1950,2002,1992,extra1,0.76
-1950,2007,1997,logistic2,0.65
-1950,1999,1984,logistic1,0.64
-1950,1997,1993,extra2,0.74
-1950,1997,1981,rf1,0.8
-1950,2001,1994,rf2,0.76
-1950,2004,1989,logistic1,0.84
-1950,2006,1981,logistic2,0.79
-1950,1995,1977,extra1,0.58
-1950,2005,1993,logistic2,0.74
-1950,2001,1976,decision1,0.64
-1950,2003,1983,decision1,0.68
-1950,2003,1989,logistic2,0.86
-1950,2003,1995,rf2,0.78
-1950,1996,1986,rf2,0.71
-1950,2007,1978,decision2,0.57
-1950,1995,1992,rf2,0.75
-1950,2006,2000,rf1,0.79
-1950,2003,1973,logistic1,0.67
-1950,1994,1979,rf2,0.62
-1950,1996,1996,decision1,0.5
-1950,2007,1988,rf1,0.75
-1950,2005,1974,rf2,0.81
-1950,2001,1979,decision1,0.61
-1950,1998,1984,decision2,0.68
-1950,2005,1990,logistic2,0.79
-1950,1995,1986,logistic2,0.64
-1950,1998,1987,decision2,0.56
-1950,2004,1974,extra1,0.78
-1950,2005,1987,extra1,0.72
-1950,1997,1978,extra1,0.71
-1950,1996,1983,rf2,0.71
-1950,1995,1980,decision1,0.63
-1950,2004,2002,logistic2,0.56
-1950,2001,1974,logistic2,0.64
-1950,2005,1977,logistic1,0.69
-1950,2004,1999,extra1,0.66
-1950,1997,1984,rf1,0.64
-1950,1997,1987,logistic2,0.6
-1950,1995,1995,rf2,0.56
-1950,2004,1983,rf2,0.83
-1950,1999,1997,extra1,0.7
-1950,2004,1986,rf2,0.74
-1950,1999,1981,rf2,0.84
-1950,1996,1977,extra2,0.69
-1950,2007,2000,extra2,0.79
-1950,2002,1982,rf2,0.76
-1950,2007,2006,rf2,0.82
-1950,1997,1975,extra1,0.72
-1950,2003,1977,rf1,0.7
-1950,2000,1997,decision2,0.63
-1950,2001,1994,logistic1,0.76
-1950,1998,1981,extra2,0.84
-1950,1997,1990,extra2,0.79
-1950,1998,1993,rf2,0.76
-1950,2001,2001,extra1,0.76
-1950,1995,1989,extra2,0.83
-1950,2000,2000,rf2,0.74
-1950,1995,1977,decision1,0.69
-1950,1995,1983,logistic2,0.59
-1950,2002,1998,decision1,0.51
-1950,2001,1991,rf2,0.83
-1950,1996,1980,decision2,0.56
-1950,2001,1988,decision2,0.6
-1950,1998,1990,rf2,0.74
-1950,1998,1975,logistic2,0.69
-1950,2002,1992,decision1,0.75
-1950,1994,1986,rf1,0.67
-1950,2005,2002,rf2,0.76
-1950,2006,1973,logistic2,0.61
-1950,2000,1994,logistic2,0.74
-1950,2006,1993,rf2,0.75
-1950,2002,1995,logistic2,0.63
-1950,1996,1983,logistic1,0.62
-1950,1999,1978,decision2,0.53
-1950,1999,1991,rf1,0.77
-1950,2005,2005,rf2,0.85
-1950,1998,1997,rf1,0.76
-1950,2003,1995,logistic1,0.69
-1950,1995,1992,logistic1,0.79
-1950,1996,1993,extra1,0.77
-1950,1996,1986,logistic1,0.65
-1950,2006,1975,decision1,0.62
-1950,2003,1992,decision2,0.77
-1950,1997,1997,rf1,0.76
-1950,1999,1994,extra1,0.78
-1950,2004,1993,rf1,0.74
-1950,1995,1995,logistic1,0.48
-1950,1997,1978,decision1,0.6
-1950,2003,1980,extra1,0.75
-1950,1995,1980,logistic2,0.67
-1950,1994,1989,extra1,0.8
-1950,1998,1978,extra2,0.82
-1950,2000,1991,extra2,0.9
-1950,2001,1976,logistic2,0.67
-1950,2003,2001,logistic1,0.75
-1950,1999,1975,decision2,0.55
-1950,2005,1980,logistic1,0.74
-1950,2007,1994,logistic2,0.76
-1950,2000,1985,extra1,0.57
-1950,2002,1979,rf2,0.77
-1950,1996,1990,rf1,0.72
-1950,2003,1986,logistic2,0.71
-1950,1997,1981,extra1,0.75
-1950,2007,1981,logistic1,0.83
-1950,2001,1985,decision2,0.53
-1950,2001,1982,decision2,0.55
-1950,1996,1996,logistic2,0.4
-1950,1998,1984,rf2,0.69
-1950,2000,1988,logistic2,0.62
-1950,2005,1984,extra1,0.67
-1950,2004,1980,decision2,0.54
-1950,2002,1974,extra1,0.72
-1950,2002,1976,decision2,0.58
-1950,2005,1974,logistic1,0.72
-1950,2006,2003,extra1,0.81
-1950,1995,1986,extra2,0.71
-1950,2007,1991,decision1,0.61
-1950,1998,1990,logistic1,0.78
-1950,2002,2001,extra2,0.82
-1950,2002,1989,rf1,0.84
-1950,1999,1997,decision1,0.61
-1950,1997,1975,decision1,0.62
-1950,1998,1987,rf2,0.74
-1950,2000,1976,rf1,0.69
-1950,2001,1979,logistic2,0.68
-1950,2004,1977,extra2,0.76
-1950,2002,1986,rf1,0.74
-1950,1999,1981,logistic1,0.8
-1950,2000,1982,rf1,0.8
-1950,1999,1988,rf1,0.66
-1950,2006,1997,rf1,0.75
-1950,2003,1983,logistic2,0.69
-1950,2002,1982,logistic1,0.77
-1950,1996,1993,decision1,0.6
-1950,2004,1983,logistic1,0.71
-1950,2000,1979,rf1,0.66
-1950,2006,1978,logistic2,0.71
-1950,2005,1999,rf2,0.77
-1950,2005,1987,decision1,0.67
-1950,1999,1985,rf1,0.6
-1950,2007,1985,rf1,0.64
-1950,2006,1981,extra2,0.8
-1950,2008,1973,rf1,0.78
-1950,2001,1998,rf1,0.63
-1950,2000,2000,logistic1,0.56
-1950,2007,2006,logistic1,0.67
-1950,1997,1987,extra2,0.71
-1950,2005,1993,extra2,0.79
-1950,1998,1993,logistic1,0.71
-1950,1997,1984,extra1,0.7
-1950,2003,1998,rf2,0.71
-1950,1997,1993,decision2,0.6
-1950,2004,1999,decision1,0.61
-1950,2001,1991,logistic1,0.81
-1950,2007,1997,extra2,0.73
-1950,2006,2006,logistic2,0.61
-1950,1999,1994,decision1,0.65
-1950,2007,2003,decision2,0.63
-1950,2007,1973,extra1,0.75
-1950,2004,1996,extra1,0.65
-1950,2006,1990,rf2,0.8
-1950,1998,1984,logistic1,0.61
-1950,2004,1986,logistic1,0.75
-1950,2005,1990,extra2,0.79
-1950,2002,1998,logistic2,0.56
-1950,2005,2002,logistic1,0.6
-1950,2003,1989,extra2,0.86
-1950,2008,1979,decision1,0.6
-1950,1995,1983,extra2,0.73
-1950,2007,1978,rf2,0.82
-1950,1997,1981,decision1,0.78
-1950,1997,1978,logistic2,0.66
-1950,1998,1981,decision2,0.57
-1950,2000,1985,decision1,0.56
-1950,2001,1974,extra2,0.77
-1950,2004,1974,decision1,0.53
-1950,1996,1980,rf2,0.67
-1950,2008,1976,extra1,0.68
-1950,2004,1990,rf1,0.78
-1950,2005,2005,logistic1,0.58
-1950,1998,1997,extra1,0.68
-1950,2001,2001,decision1,0.61
-1950,1996,1977,decision2,0.5
-1950,2008,1994,logistic1,0.76
-1950,1999,1978,rf2,0.82
-1950,1998,1975,extra2,0.76
-1950,1995,1989,decision2,0.72
-1950,1997,1997,extra1,0.7
-1950,2002,1979,logistic1,0.7
-1950,2004,2002,extra2,0.69
-1950,1999,1991,extra1,0.82
-1950,1998,1987,logistic1,0.65
-1950,2001,1988,rf2,0.78
-1950,1996,1984,rf1,0.64
-1950,1999,1997,logistic2,0.64
-1950,2002,1995,extra2,0.73
-1950,2005,1996,decision2,0.56
-1950,1996,1993,logistic2,0.66
-1950,2006,1993,logistic1,0.74
-1950,2000,1997,rf2,0.75
-1950,1996,1987,rf1,0.63
-1950,1995,1986,decision2,0.56
-1950,2007,1988,extra1,0.71
-1950,2002,1992,logistic2,0.77
-1950,2005,1984,decision1,0.64
-1950,2006,1973,extra2,0.77
-1950,2003,1980,decision1,0.59
-1950,2003,1974,rf1,0.74
-1950,2000,1988,extra2,0.8
-1950,1998,1978,decision2,0.5
-1950,2005,1999,logistic1,0.62
-1950,2000,1994,extra2,0.76
-1950,1997,1975,logistic2,0.69
-1950,2006,1984,decision2,0.66
-1950,2002,1974,decision1,0.41
-1950,1996,1996,extra2,0.54
-1950,2007,1973,decision1,0.58
-1950,2004,1993,extra1,0.72
-1950,1997,1984,decision1,0.71
-1950,2005,1973,decision2,0.44
-1950,1996,1980,logistic1,0.66
-1950,2001,1979,extra2,0.78
-1950,2007,1975,decision2,0.5
-1950,1996,1990,extra1,0.8
-1950,2000,1991,decision2,0.57
-1950,2006,1975,logistic2,0.72
-1950,2001,1985,rf2,0.66
-1950,1999,1975,rf2,0.79
-1950,2002,1976,rf2,0.72
-1950,1997,1990,decision2,0.63
-1950,2001,1995,rf1,0.71
-1950,2003,1996,rf1,0.68
-1950,2006,2000,extra1,0.76
-1950,1997,1993,rf2,0.76
-1950,2005,1978,rf1,0.76
-1950,2006,2003,decision1,0.72
-1950,2003,1977,extra1,0.69
-1950,2003,1998,logistic1,0.59
-1950,2000,1982,extra1,0.76
-1950,1998,1991,rf1,0.76
-1950,2008,1985,extra2,0.68
-1950,2004,1980,rf2,0.77
-1950,2007,1991,logistic2,0.81
-1950,2001,1976,extra2,0.69
-1950,2006,1987,decision2,0.53
-1950,2001,1982,rf2,0.77
-1950,1999,1994,logistic2,0.72
-1950,1999,1988,extra1,0.71
-1950,1997,1997,decision1,0.56
-1950,2008,1982,logistic2,0.77
-1950,2003,1992,rf2,0.76
-1950,1999,1978,logistic1,0.69
-1950,2008,1988,decision2,0.55
-1950,1998,1997,decision1,0.55
-1950,2004,1996,decision1,0.41
-1950,2003,1986,extra2,0.75
-1950,1998,1985,rf1,0.59
-1950,2001,1998,extra1,0.64
-1950,1997,1981,logistic2,0.74
-1950,1999,1991,decision1,0.63
-1950,2005,1987,logistic2,0.64
-1950,2000,1985,logistic2,0.58
-1950,2001,1988,logistic1,0.67
-1950,2008,1976,decision1,0.66
-1950,2002,1983,rf1,0.75
-1950,2000,1979,extra1,0.69
-1950,2006,1990,logistic1,0.79
-1950,1996,1990,decision1,0.7
-1950,1997,1978,extra2,0.79
-1950,1998,1981,rf2,0.83
-1950,1995,1989,rf2,0.83
-1950,1999,1985,extra1,0.6
-1950,1998,1994,rf1,0.71
-1950,1999,1982,rf1,0.82
-1950,2003,1983,extra2,0.81
-1950,2003,1977,decision1,0.54
-1950,1997,1987,decision2,0.57
-1950,2002,2001,decision2,0.7
-1950,2003,2002,rf1,0.65
-1950,2007,2000,decision2,0.6
-1950,1996,1977,rf2,0.71
-1950,2001,2001,logistic2,0.62
-1950,2008,1991,rf2,0.89
-1950,2000,1976,extra1,0.65
-1950,2002,1986,extra1,0.78
-1950,2004,1999,logistic2,0.64
-1950,1996,1993,extra2,0.76
-1950,2002,1976,logistic1,0.68
-1950,2007,1985,extra1,0.6
-1950,1996,1996,decision2,0.48
-1950,2007,1994,extra2,0.76
-1950,2008,2004,decision1,0.62
-1950,1998,1978,rf2,0.8
-1950,2000,1997,logistic1,0.62
-1950,1997,1993,logistic1,0.73
-1950,1998,1975,decision2,0.52
-1950,2002,1989,extra1,0.82
-1950,1999,1975,logistic1,0.72
-1950,2001,1974,decision2,0.54
-1950,2004,1984,rf1,0.66
-1950,2007,1988,decision1,0.71
-1950,1996,1984,extra1,0.66
-1950,1998,1988,rf1,0.65
-1950,2006,1978,extra2,0.83
-1950,2002,1998,extra2,0.62
-1950,2005,1984,logistic2,0.66
-1950,2007,1978,logistic1,0.75
-1950,2005,1975,rf1,0.79
-1950,2005,1981,rf1,0.83
-1950,2000,1991,rf2,0.82
-1950,1999,1988,decision1,0.6
-1950,1997,1984,logistic2,0.71
-1950,2007,1982,rf1,0.81
-1950,2006,1981,decision2,0.71
-1950,2001,1992,rf1,0.77
-1950,2005,1990,decision2,0.66
-1950,1999,1997,extra2,0.73
-1950,2004,1974,logistic2,0.65
-1950,2006,1997,extra1,0.72
-1950,2001,1985,logistic1,0.62
-1950,1996,1987,extra1,0.63
-1950,2000,1982,decision1,0.71
-1950,2008,1998,rf1,0.65
-1950,2004,1977,decision2,0.56
-1950,2006,2000,decision1,0.6
-1950,1996,1981,rf1,0.82
-1950,2005,1993,decision2,0.63
-1950,2001,1982,logistic1,0.78
-1950,2008,1995,rf1,0.74
-1950,2002,1974,logistic2,0.67
-1950,2004,1993,decision1,0.67
-1950,2004,1987,rf1,0.72
-1950,2003,1989,decision2,0.73
-1950,2008,1979,logistic2,0.68
-1950,2008,2001,extra1,0.78
-1950,2002,1995,decision2,0.48
-1950,2006,2006,extra2,0.75
-1950,2000,1979,decision1,0.64
-1950,1996,1990,logistic2,0.71
-1950,1999,1991,logistic2,0.8
-1950,2004,1990,extra1,0.76
-1950,2002,1992,extra2,0.72
-1950,1999,1985,decision1,0.6
-1950,2006,1975,extra2,0.79
-1950,2005,2000,rf1,0.79
-1950,2004,1980,logistic1,0.74
-1950,1997,1990,rf2,0.77
-1950,1996,1977,logistic1,0.6
-1950,2006,2003,logistic2,0.73
-1950,2000,1988,decision2,0.65
-1950,2007,2003,rf2,0.86
-1950,2003,1980,logistic2,0.71
-1950,2007,1997,decision2,0.59
-1950,2001,1998,decision1,0.44
-1950,2002,1980,rf1,0.68
-1950,2005,2003,rf1,0.83
-1950,2008,1973,extra1,0.77
-1950,2008,2007,logistic2,0.67
-1950,2007,1973,logistic2,0.64
-1950,1998,1991,extra1,0.84
-1950,2003,1992,logistic1,0.76
-1950,2000,1976,decision1,0.61
-1950,2005,1996,rf2,0.76
-1950,1998,1997,logistic2,0.61
-1950,1998,1981,logistic1,0.78
-1950,1998,1978,logistic1,0.64
-1950,1999,1994,extra2,0.79
-1950,2007,2007,rf1,0.86
-1950,1997,1981,extra2,0.86
-1950,2004,2002,decision2,0.6
-1950,2000,1994,decision2,0.66
-1950,2002,1989,decision1,0.78
-1950,2009,1989,decision1,0.71
-1950,1999,1979,rf1,0.68
-1950,2006,1984,rf2,0.77
-1950,1996,1987,decision1,0.56
-1950,1999,1988,logistic2,0.64
-1950,2001,1995,extra1,0.69
-1950,1998,1985,extra1,0.58
-1950,2003,1974,extra1,0.73
-1950,1998,1994,extra1,0.74
-1950,2001,1976,decision2,0.57
-1950,2006,1994,rf1,0.75
-1950,2005,1973,rf2,0.8
-1950,2002,1986,decision1,0.67
-1950,1997,1987,rf2,0.72
-1950,2004,1996,logistic2,0.6
-1950,2005,1987,extra2,0.71
-1950,2002,1983,extra1,0.76
-1950,2009,1973,rf1,0.76
-1950,2007,1985,decision1,0.61
-1950,2001,1989,rf1,0.81
-1950,2001,1979,decision2,0.54
-1950,2000,1991,logistic1,0.83
-1950,2000,1985,extra2,0.63
-1950,1998,1975,rf2,0.79
-1950,2003,1996,extra1,0.62
-1950,2008,1991,logistic1,0.82
-1950,2007,1975,rf2,0.81
-1950,2006,1973,decision2,0.44
-1950,1998,1988,extra1,0.7
-1950,1997,1990,logistic1,0.76
-1950,2003,1977,logistic2,0.67
-1950,2009,1976,rf2,0.77
-1950,2005,1978,extra1,0.77
-1950,2009,1979,logistic1,0.72
-1950,2006,1987,rf2,0.79
-1950,1999,1985,logistic2,0.56
-1950,2008,1988,rf2,0.83
-1950,2002,1977,rf1,0.72
-1950,2003,1999,rf1,0.71
-1950,1997,1994,rf1,0.69
-1950,2005,1996,logistic1,0.57
-1950,1999,1982,extra1,0.78
-1950,2000,1998,rf1,0.59
-1950,1997,1984,extra2,0.67
-1950,2001,2001,extra2,0.81
-1950,2007,2000,rf2,0.73
-1950,2002,2001,rf2,0.82
-1950,1999,1997,decision2,0.59
-1950,1999,1991,extra2,0.9
-1950,1998,1991,decision1,0.67
-1950,2003,1983,decision2,0.6
-1950,2007,2003,logistic1,0.77
-1950,2000,1982,logistic2,0.75
-1950,2007,1991,extra2,0.9
-1950,2008,2004,logistic2,0.7
-1950,2008,1982,extra2,0.8
-1950,1999,1976,rf1,0.68
-1950,2004,1990,decision1,0.66
-1950,2007,1988,logistic2,0.69
-1950,2008,1976,logistic2,0.68
-1950,1997,1987,logistic1,0.62
-1950,2001,1974,rf2,0.77
-1950,2004,1974,extra2,0.79
-1950,2006,1997,decision1,0.68
-1950,2003,2002,extra1,0.69
-1950,2000,1979,logistic2,0.67
-1950,1998,1985,decision1,0.59
-1950,2004,1999,extra2,0.7
-1950,1998,1994,decision1,0.57
-1950,2008,1985,decision2,0.57
-1950,1998,1975,logistic1,0.7
-1950,2002,1995,rf2,0.78
-1950,2008,2001,decision1,0.69
-1950,2002,1998,decision2,0.53
-1950,2006,2000,logistic2,0.59
-1950,2005,1993,rf2,0.8
-1950,2004,1993,logistic2,0.74
-1950,2008,1973,decision1,0.63
-1950,2001,1992,extra1,0.76
-1950,2003,1989,rf2,0.9
-1950,2004,1977,rf2,0.77
-1950,2002,1974,extra2,0.77
-1950,2003,1980,extra2,0.75
-1950,2005,1978,decision1,0.64
-1950,2001,1998,logistic2,0.54
-1950,1998,1997,extra2,0.71
-1950,2000,1988,rf2,0.76
-1950,2007,1975,logistic1,0.73
-1950,1999,1988,extra2,0.75
-1950,2005,1984,extra2,0.69
-1950,2003,1974,decision1,0.66
-1950,2005,1990,rf2,0.81
-1950,2006,1981,rf2,0.81
-1950,2000,1976,logistic2,0.65
-1950,2003,1986,decision2,0.57
-1950,2002,1989,logistic2,0.83
-1950,2000,1994,rf2,0.76
-1950,2005,1975,extra1,0.78
-1950,2001,1986,rf1,0.75
-1950,1998,1982,rf1,0.81
-1950,2006,1984,logistic1,0.65
-1950,2002,1983,decision1,0.65
-1950,2009,1992,logistic2,0.78
-1950,2001,1995,decision1,0.55
-1950,1997,1981,decision2,0.7
-1950,1998,1979,rf1,0.68
-1950,2001,1983,rf1,0.73
-1950,1999,1994,decision2,0.61
-1950,1998,1988,decision1,0.61
-1950,2006,1991,rf1,0.77
-1950,2006,2003,extra2,0.85
-1950,2004,1984,extra1,0.66
-1950,2009,2004,logistic1,0.71
-1950,1999,1982,decision1,0.7
-1950,2004,1987,extra1,0.68
-1950,2005,1981,extra1,0.84
-1950,2005,2000,extra1,0.71
-1950,2002,2001,logistic1,0.74
-1950,2002,1986,logistic2,0.69
-1950,2005,1973,logistic1,0.68
-1950,2003,1993,rf1,0.74
-1950,1999,1979,extra1,0.69
-1950,2002,1980,extra1,0.73
-1950,2009,1973,decision2,0.53
-1950,2009,1995,extra2,0.72
-1950,2001,1989,extra1,0.81
-1950,2004,1996,extra2,0.67
-1950,2001,1979,rf2,0.8
-1950,2009,1986,extra1,0.79
-1950,1998,1994,logistic2,0.71
-1950,2007,1982,extra1,0.75
-1950,2003,1977,extra2,0.77
-1950,2004,2002,rf2,0.79
-1950,1998,1985,logistic2,0.57
-1950,2003,1996,decision1,0.55
-1950,2007,1979,rf1,0.7
-1950,2001,1976,rf2,0.73
-1950,2006,1987,logistic1,0.67
-1950,1997,1991,rf1,0.74
-1950,1998,1991,logistic2,0.78
-1950,1999,1985,extra2,0.63
-1950,2008,1995,extra1,0.71
-1950,2002,1992,decision2,0.71
-1950,2002,1977,extra1,0.66
-1950,2008,1979,extra2,0.75
-1950,2007,1994,decision2,0.68
-1950,2001,1992,decision1,0.79
-1950,2003,2002,decision1,0.6
-1950,2007,1997,rf2,0.79
-1950,2005,2003,extra1,0.8
-1950,1999,1997,rf2,0.79
-1950,2008,2007,extra2,0.79
-1950,2002,1995,logistic1,0.7
-1950,2009,1989,logistic2,0.85
-1950,2009,1983,rf1,0.81
-1950,2000,1992,rf1,0.8
-1950,2004,1981,rf1,0.83
-1950,2008,1998,extra1,0.65
-1950,2008,1988,logistic1,0.68
-1950,2009,2001,rf2,0.82
-1950,2003,1989,logistic1,0.85
-1950,2000,1985,decision2,0.56
-1950,2007,1973,extra2,0.81
-1950,2000,1988,logistic1,0.66
-1950,2007,1985,logistic2,0.61
-1950,2006,1975,decision2,0.56
-1950,2004,1977,logistic1,0.7
-1950,1997,1994,extra1,0.75
-1950,1997,1984,decision2,0.62
-1950,2009,1998,decision2,0.58
-1950,2009,1973,extra1,0.76
-1950,2010,1974,decision1,0.46
-1950,2006,2006,decision2,0.59
-1950,2006,1978,decision2,0.61
-1950,1999,1979,decision1,0.57
-1950,2001,1995,logistic2,0.62
-1950,2003,1983,rf2,0.83
-1950,2000,1998,extra1,0.63
-1950,2009,1976,logistic1,0.68
-1950,2007,2007,extra1,0.79
-1950,2005,1993,logistic1,0.74
-1950,1999,1991,decision2,0.6
-1950,2001,2001,decision2,0.64
-1950,2004,1990,logistic2,0.77
-1950,1999,1976,extra1,0.65
-1950,2000,1979,extra2,0.76
-1950,2006,1994,extra1,0.78
-1950,2000,1982,extra2,0.77
-1950,2000,1994,logistic1,0.75
-1950,2006,1973,rf2,0.81
-1950,1998,1976,rf1,0.65
-1950,2004,1987,decision1,0.52
-1950,2007,1988,extra2,0.82
-1950,1999,1988,decision2,0.61
-1950,2001,1976,logistic1,0.69
-1950,2005,1975,decision1,0.7
-1950,2005,2000,decision1,0.66
-1950,2005,1981,decision1,0.63
-1950,2005,1987,decision2,0.55
-1950,2007,2000,logistic1,0.58
-1950,2003,1999,extra1,0.7
-1950,2005,1990,logistic1,0.79
-1950,2002,1980,decision1,0.66
-1950,2005,1984,decision2,0.59
-1950,1998,1988,logistic2,0.65
-1950,2002,1998,rf2,0.66
-1950,2001,1989,decision1,0.72
-1950,1999,1982,logistic2,0.75
-1950,2001,1998,extra2,0.62
-1950,2008,2001,logistic2,0.7
-1950,1999,1994,rf2,0.76
-1950,1998,1982,extra1,0.75
-1950,2004,1984,decision1,0.61
-1950,2002,1977,decision1,0.69
-1950,2002,1983,logistic2,0.69
-1950,2000,1976,extra2,0.71
-1950,1999,1997,logistic1,0.6
-1950,1998,1979,extra1,0.69
-1950,2001,1979,logistic1,0.68
-1950,2008,1976,extra2,0.72
-1950,2003,1986,rf2,0.76
-1950,2006,1981,logistic1,0.81
-1950,1998,1997,decision2,0.58
-1950,2001,1992,logistic2,0.78
-1950,2008,1985,rf2,0.71
-1950,2001,1983,extra1,0.74
-1950,2001,1986,extra1,0.78
-1950,2002,1989,extra2,0.89
-1950,2004,2002,logistic1,0.61
-1950,2008,1982,decision2,0.53
-1950,2005,1978,logistic2,0.73
-1950,2006,2000,extra2,0.81
-1950,2006,1997,logistic2,0.66
-1950,2004,1993,extra2,0.8
-1950,2005,1997,rf1,0.75
-1950,2003,2002,logistic2,0.58
-1950,1998,1991,extra2,0.88
-1950,2007,2004,rf1,0.77
-1950,1998,1994,extra2,0.8
-1950,2007,1997,logistic1,0.66
-1950,2003,1974,logistic2,0.67
-1950,2007,1982,decision1,0.59
-1950,2008,1992,rf1,0.75
-1950,2008,2004,extra2,0.77
-1950,2002,1986,extra2,0.75
-1950,2008,1995,decision1,0.49
-1950,1997,1984,rf2,0.65
-1950,2005,2003,decision1,0.82
-1950,2009,1986,decision1,0.63
-1950,2009,1973,decision1,0.53
-1950,2008,1973,logistic2,0.61
-1950,2009,1980,rf1,0.77
-1950,2003,1996,logistic2,0.59
-1950,2003,1993,extra1,0.72
-1950,2007,1991,decision2,0.58
-1950,2007,1976,rf1,0.71
-1950,2003,1999,decision1,0.52
-1950,1999,1994,logistic1,0.75
-1950,2004,1974,decision2,0.5
-1950,1998,1982,decision1,0.69
-1950,2003,1983,logistic1,0.72
-1950,1999,1979,logistic2,0.66
-1950,2000,1985,rf2,0.63
-1950,2009,2001,logistic1,0.74
-1950,2000,1992,extra1,0.73
-1950,2006,1994,decision1,0.6
-1950,2002,2002,rf1,0.66
-1950,2002,1998,logistic1,0.57
-1950,2004,1999,decision2,0.63
-1950,2001,1983,decision1,0.65
-1950,2000,1998,decision1,0.54
-1950,2003,1980,decision2,0.55
-1950,2002,1974,decision2,0.56
-1950,2006,1991,extra1,0.85
-1950,2008,1998,decision1,0.59
-1950,2001,1986,decision1,0.67
-1950,2009,2008,rf1,0.81
-1950,2006,1985,rf1,0.65
-1950,2002,1992,rf2,0.76
-1950,2010,1974,logistic2,0.67
-1950,2001,1995,extra2,0.75
-1950,2009,1992,extra2,0.69
-1950,1999,1976,decision1,0.55
-1950,1999,1991,rf2,0.83
-1950,2001,1989,logistic2,0.82
-1950,1998,1988,extra2,0.74
-1950,2003,1990,rf1,0.79
-1950,2002,1980,logistic2,0.68
-1950,2001,2001,rf2,0.81
-1950,2010,1973,rf1,0.76
-1950,2002,1996,rf1,0.67
-1950,2003,1986,logistic1,0.74
-1950,1999,1988,rf2,0.74
-1950,2000,1982,decision2,0.61
-1950,2003,1977,decision2,0.52
-1950,2007,1985,extra2,0.7
-1950,2006,1973,logistic1,0.67
-1950,2005,1974,rf1,0.75
-1950,1999,1985,decision2,0.59
-1950,2010,1977,logistic2,0.7
-1950,2007,2007,decision1,0.72
-1950,2004,1981,extra1,0.83
-1950,2007,1979,extra1,0.69
-1950,2000,1979,decision2,0.47
-1950,2005,1975,logistic2,0.73
-1950,2000,1989,rf1,0.82
-1950,2000,1995,rf1,0.71
-1950,2002,1977,logistic2,0.66
-1950,2010,1989,logistic1,0.84
-1950,2001,1977,rf1,0.71
-1950,2004,1978,rf1,0.76
-1950,2006,1978,rf2,0.82
-1950,2007,1994,rf2,0.76
-1950,2004,1984,logistic2,0.68
-1950,2004,1996,decision2,0.55
-1950,2004,1990,extra2,0.79
-1950,2009,2005,rf1,0.75
-1950,2004,1987,logistic2,0.65
-1950,1999,1982,extra2,0.77
-1950,2006,1988,rf1,0.74
-1950,2001,1992,extra2,0.76
-1950,1998,1997,rf2,0.75
-1950,2009,1989,extra2,0.89
-1950,2010,1980,extra2,0.77
-1950,2005,2000,logistic2,0.6
-1950,1998,1991,decision2,0.56
-1950,2006,1975,rf2,0.79
-1950,2005,1984,rf2,0.79
-1950,2008,1985,logistic1,0.57
-1950,2005,1978,extra2,0.79
-1950,2001,1998,decision2,0.53
-1950,2002,1983,extra2,0.81
-1950,2009,1983,extra1,0.78
-1950,2005,1987,rf2,0.74
-1950,2006,2006,rf2,0.82
-1950,1999,1998,rf1,0.59
-1950,2009,1973,rf2,0.83
-1950,2000,1976,decision2,0.53
-1950,1999,1991,logistic1,0.8
-1950,2006,2003,decision2,0.65
-1950,2003,1993,decision1,0.68
-1950,2001,1980,rf1,0.69
-1950,2005,1981,logistic2,0.77
-1950,2000,1985,logistic1,0.6
-1950,2000,1992,decision1,0.79
-1950,2008,1989,rf1,0.85
-1950,2009,1998,rf2,0.71
-1950,1999,1988,logistic1,0.67
-1950,2001,2001,logistic1,0.68
-1950,2008,2001,extra2,0.79
-1950,2003,2002,extra2,0.73
-1950,2002,1992,logistic1,0.79
-1950,2009,1995,decision2,0.39
-1950,1999,1976,logistic2,0.64
-1950,2001,1986,logistic2,0.71
-1950,2003,1974,extra2,0.78
-1950,2006,1994,logistic2,0.76
-1950,2000,1998,logistic2,0.57
-1950,2009,1977,rf1,0.78
-1950,2008,1979,decision2,0.53
-1950,2008,1995,logistic2,0.64
-1950,2009,1986,logistic2,0.71
-1950,2005,2003,logistic2,0.73
-1950,2006,1978,logistic1,0.74
-1950,2005,1991,rf1,0.78
-1950,2001,1983,logistic2,0.67
-1950,2010,1986,rf2,0.74
-1950,2005,1994,rf1,0.76
-1950,2006,1991,decision1,0.76
-1950,2008,1982,rf2,0.8
-1950,2007,1973,decision2,0.48
-1950,1998,1994,decision2,0.64
-1950,1998,1997,logistic1,0.54
-1950,2007,2001,rf1,0.87
-1950,2006,1997,extra2,0.75
-1950,2003,1999,logistic2,0.63
-1950,2005,1987,logistic1,0.68
-1950,2007,1982,logistic2,0.78
-1950,1999,1979,extra2,0.73
-1950,2004,1981,decision1,0.76
-1950,2002,1989,decision2,0.69
-1950,2004,2003,rf1,0.82
-1950,2006,1982,rf1,0.81
-1950,2004,1993,decision2,0.53
-1950,1999,1995,rf1,0.7
-1950,2007,1994,logistic1,0.76
-1950,2009,1973,logistic2,0.67
-1950,2003,1996,extra2,0.68
-1950,2008,1998,logistic2,0.59
-1950,2004,1974,rf2,0.8
-1950,2005,1997,extra1,0.71
-1950,2005,1984,logistic1,0.64
-1950,2006,1975,logistic1,0.73
-1950,2002,1980,extra2,0.77
-1950,2002,1974,rf2,0.78
-1950,2001,1989,extra2,0.87
-1950,1999,1985,rf2,0.64
-1950,2008,2007,decision2,0.62
-1950,2008,1976,decision2,0.44
-1950,2008,1992,extra1,0.7
-1950,2003,1980,rf2,0.78
-1950,2000,1982,rf2,0.76
-1950,2007,2004,extra1,0.81
-1950,2007,1998,rf1,0.63
-1950,2004,1999,rf2,0.76
-1950,2008,1973,extra2,0.8
-1950,2002,1977,extra2,0.77
-1950,2000,1992,logistic2,0.79
-1950,2002,2002,extra1,0.61
-1950,2000,1989,extra1,0.84
-1950,2000,1979,rf2,0.77
-1950,2007,1991,rf2,0.89
-1950,2007,2007,logistic2,0.68
-1950,2010,1993,rf1,0.75
-1950,2003,1977,rf2,0.77
-1950,2000,1995,extra1,0.69
-1950,2007,1979,decision1,0.7
-1950,2007,1976,extra1,0.7
-1950,2001,1995,decision2,0.4
-1950,2001,1977,extra1,0.68
-1950,2003,1984,rf1,0.66
-1950,2006,2006,logistic1,0.67
-1950,2003,1993,logistic2,0.72
-1950,2002,1999,rf1,0.71
-1950,2009,1983,decision1,0.64
-1950,2010,1983,decision2,0.63
-1950,2009,1980,extra1,0.74
-1950,2002,1986,decision2,0.55
-1950,2010,1974,extra2,0.77
-1950,1999,1998,extra1,0.62
-1950,2008,2004,decision2,0.72
-1950,2003,1990,extra1,0.78
-1950,2003,1987,rf1,0.72
-1950,2008,1982,logistic1,0.76
-1950,2002,1996,extra1,0.66
-1950,2004,1984,extra2,0.7
-1950,2004,1987,extra2,0.73
-1950,2000,1976,rf2,0.71
-1950,2006,1985,extra1,0.58
-1950,2009,1998,logistic1,0.58
-1950,1999,1992,rf1,0.77
-1950,2005,1975,extra2,0.78
-1950,2009,2008,extra1,0.73
-1950,1999,1976,extra2,0.72
-1950,2000,1982,logistic1,0.78
-1950,2005,1997,decision1,0.61
-1950,2000,1998,extra2,0.61
-1950,2002,1974,logistic1,0.71
-1950,2001,1998,rf2,0.64
-1950,2000,1986,rf1,0.75
-1950,2007,1988,decision2,0.63
-1950,2006,2000,decision2,0.62
-1950,2005,1974,extra1,0.77
-1950,2004,1978,extra1,0.74
-1950,2001,1980,extra1,0.73
-1950,1999,1982,decision2,0.56
-1950,1998,1994,rf2,0.75
-1950,2005,1981,extra2,0.79
-1950,2010,1977,extra2,0.74
-1950,2010,1996,extra1,0.66
-1950,2004,1990,decision2,0.69
-1950,2004,1996,rf2,0.74
-1950,2009,1973,logistic1,0.69
-1950,2009,2002,rf1,0.72
-1950,2001,1986,extra2,0.75
-1950,1999,1985,logistic1,0.61
-1950,2001,1992,decision2,0.73
-1950,2010,1999,decision1,0.59
-1950,2000,1979,logistic1,0.67
-1950,2006,1991,logistic2,0.81
-1950,2000,1989,decision1,0.76
-1950,2006,1974,rf1,0.73
-1950,2007,2004,decision1,0.71
-1950,1999,1989,rf1,0.82
-1950,2003,1999,extra2,0.69
-1950,2005,2000,extra2,0.8
-1950,2002,1993,rf1,0.73
-1950,2006,1988,extra1,0.72
-1950,2001,1977,decision1,0.62
-1950,2010,2002,logistic2,0.58
-1950,2008,1992,decision1,0.69
-1950,2002,1983,decision2,0.53
-1950,2001,1983,extra2,0.81
-1950,2003,1977,logistic1,0.69
-1950,2002,1996,decision1,0.53
-1950,1999,1979,decision2,0.49
-1950,2004,1981,logistic2,0.76
-1950,2006,2003,rf2,0.86
-1950,2003,1980,logistic1,0.74
-1950,2009,2005,extra1,0.72
-1950,2010,2005,extra2,0.78
-1950,2000,1995,decision1,0.52
-1950,2004,1999,logistic1,0.62
-1950,2002,1989,rf2,0.91
-1950,2005,1991,extra1,0.85
-1950,2004,1974,logistic1,0.71
-1950,1999,1995,extra1,0.67
-1950,2005,2003,extra2,0.84
-1950,2002,2002,decision1,0.58
-1950,2009,1995,rf2,0.74
-1950,2004,1993,rf2,0.8
-1950,2006,1994,extra2,0.75
-1950,2008,1995,extra2,0.72
-1950,2009,1992,decision2,0.72
-1950,2010,1973,extra1,0.74
-1950,2007,1982,extra2,0.81
-1950,2005,1978,decision2,0.54
-1950,1999,1998,decision1,0.53
-1950,2003,1990,decision1,0.71
-1950,2000,1992,extra2,0.77
-1950,2003,1974,decision2,0.55
-1950,2009,2008,decision1,0.62
-1950,2004,1978,decision1,0.64
-1950,2001,1980,decision1,0.6
-1950,2000,1976,logistic1,0.67
-1950,2010,1986,logistic1,0.73
-1950,2005,1988,rf1,0.73
-1950,2009,1977,extra1,0.7
-1950,2004,2003,extra1,0.83
-1950,2007,1973,rf2,0.8
-1950,2005,1994,extra1,0.79
-1950,2010,1990,rf1,0.73
-1950,2007,2001,extra1,0.77
-1950,2007,1976,decision1,0.66
-1950,2009,1986,extra2,0.74
-1950,2008,1979,rf2,0.8
-1950,2005,1985,rf1,0.62
-1950,2007,1985,decision2,0.59
-1950,2006,1979,rf1,0.69
-1950,2000,1998,decision2,0.52
-1950,2003,1996,decision2,0.53
-1950,2008,1989,extra1,0.86
-1950,2001,1995,rf2,0.78
-1950,2004,1996,logistic1,0.57
-1950,2001,1989,decision2,0.69
-1950,2006,1985,decision1,0.52
-1950,2000,1989,logistic2,0.82
-1950,2006,1982,extra1,0.76
-1950,2008,1986,rf1,0.74
-1950,2009,1973,extra2,0.78
-1950,2008,2007,rf2,0.83
-1950,2002,1986,rf2,0.78
-1950,2005,1997,logistic2,0.65
-1950,2009,1980,decision1,0.62
-1950,2006,1997,decision2,0.61
-1950,1999,1982,rf2,0.78
-1950,2002,1999,extra1,0.68
-1950,1999,1995,decision1,0.52
-1950,2008,1998,extra2,0.64
-1950,2001,1998,logistic1,0.54
-1950,2003,1993,extra2,0.79
-1950,2007,1998,extra1,0.64
-1950,2003,2002,decision2,0.57
-1950,2010,1980,decision2,0.52
-1950,2001,1977,logistic2,0.65
-1950,2008,2001,decision2,0.73
-1950,1999,1976,decision2,0.62
-1950,2006,1976,rf1,0.7
-1950,2007,1995,rf1,0.74
-1950,1999,1992,extra1,0.74
-1950,2003,1984,extra1,0.66
-1950,2002,1977,decision2,0.48
-1950,2002,1980,decision2,0.54
-1950,2004,1984,decision2,0.71
-1950,2009,1983,logistic2,0.7
-1950,2007,2007,extra2,0.81
-1950,2009,1989,decision2,0.81
-1950,2001,1992,rf2,0.76
-1950,1999,1989,extra1,0.84
-1950,2010,1996,decision1,0.5
-1950,2000,1995,logistic2,0.6
-1950,2007,1991,logistic1,0.82
-1950,2011,1974,logistic1,0.74
-1950,1999,1998,logistic2,0.55
-1950,2006,1988,decision1,0.65
-1950,2002,1989,logistic1,0.84
-1950,2008,1976,rf2,0.73
-1950,2003,1999,decision2,0.62
-1950,2000,1986,extra1,0.76
-1950,2002,1996,logistic2,0.58
-1950,2000,1983,rf1,0.73
-1950,2010,2008,decision2,0.73
-1950,2005,1991,decision1,0.68
-1950,1999,1979,rf2,0.76
-1950,2003,1987,extra1,0.68
-1950,2008,1992,logistic2,0.79
-1950,1999,1986,rf1,0.75
-1950,2009,2005,decision1,0.58
-1950,2005,1994,decision1,0.67
-1950,2006,2003,logistic1,0.75
-1950,2009,1977,decision1,0.56
-1950,2010,1983,rf2,0.85
-1950,2002,1993,extra1,0.71
-1950,2002,1975,rf1,0.74
-1950,2002,2002,logistic2,0.58
-1950,2001,1986,decision2,0.66
-1950,2000,1980,rf1,0.69
-1950,2007,1979,logistic2,0.71
-1950,1999,1982,logistic1,0.77
-1950,2001,1980,logistic2,0.68
-1950,2001,1983,decision2,0.6
-1950,2004,2003,decision1,0.76
-1950,2004,1981,extra2,0.84
-1950,2004,1993,logistic1,0.75
-1950,2006,2000,rf2,0.74
-1950,2010,1993,extra1,0.77
-1950,2007,2004,logistic2,0.69
-1950,2008,2004,rf2,0.78
-1950,2000,1992,decision2,0.76
-1950,2007,1973,logistic1,0.67
-1950,2008,1979,logistic1,0.71
-1950,2010,1974,decision2,0.56
-1950,2002,1983,rf2,0.82
-1950,2007,2001,decision1,0.68
-1950,2003,1990,logistic2,0.78
-1950,2004,1990,rf2,0.79
-1950,2008,1983,rf1,0.78
-1950,2010,1999,logistic2,0.65
-1950,2005,2003,decision2,0.6
-1950,2001,1995,logistic1,0.68
-1950,2010,1973,decision1,0.71
-1950,2003,1978,rf1,0.72
-1950,2008,1989,decision1,0.76
-1950,2006,1991,extra2,0.9
-1950,2003,1974,rf2,0.77
-1950,2002,1986,logistic1,0.71
-1950,2000,1998,rf2,0.67
-1950,2002,1999,decision1,0.52
-1950,2009,1999,rf1,0.72
-1950,2008,1973,decision2,0.48
-1950,2006,1982,decision1,0.64
-1950,2001,1992,logistic1,0.79
-1950,2000,1989,extra2,0.89
-1950,2000,1986,decision1,0.76
-1950,2005,2000,decision2,0.64
-1950,2004,1987,decision2,0.57
-1950,2007,1988,rf2,0.8
-1950,2006,1974,extra1,0.77
-1950,2003,1981,rf1,0.85
-1950,2004,1978,logistic2,0.7
-1950,1999,1995,logistic2,0.57
-1950,1999,1992,decision1,0.73
-1950,2008,2007,logistic1,0.7
-1950,2009,1995,logistic1,0.76
-1950,2003,1984,decision1,0.69
-1950,2006,1985,logistic2,0.61
-1950,2007,1976,logistic2,0.64
-1950,2007,1998,decision1,0.63
-1950,2004,1975,rf1,0.76
-1950,2005,1975,decision2,0.53
-1950,2000,1977,rf1,0.71
-1950,2003,1987,decision1,0.58
-1950,2004,2000,rf1,0.77
-1950,2009,2002,extra1,0.64
-1950,2009,1974,rf1,0.78
-1950,2006,1994,decision2,0.61
-1950,2001,1977,extra2,0.74
-1950,2010,2002,extra2,0.71
-1950,2011,1973,rf1,0.76
-1950,2002,1993,decision1,0.62
-1950,2005,1978,rf2,0.81
-1950,2003,1996,rf2,0.74
-1950,2009,2008,logistic2,0.65
-1950,2001,1989,rf2,0.9
-1950,2005,1988,extra1,0.71
-1950,2010,1983,logistic1,0.72
-1950,2006,1979,extra1,0.68
-1950,2002,1977,rf2,0.76
-1950,2003,1974,logistic1,0.71
-1950,1999,1998,extra2,0.61
-1950,2005,1994,logistic2,0.76
-1950,2000,1995,extra2,0.73
-1950,2005,1991,logistic2,0.81
-1950,2005,1985,extra1,0.57
-1950,2001,1999,rf1,0.68
-1950,2000,1998,logistic1,0.54
-1950,2004,1990,logistic1,0.78
-1950,2002,1983,logistic1,0.69
-1950,2005,1981,decision2,0.68
-1950,2011,1984,decision1,0.7
-1950,2008,2004,logistic1,0.71
-1950,2008,1986,extra1,0.78
-1950,2002,1980,rf2,0.77
-1950,2009,1992,rf2,0.82
-1950,2007,1985,rf2,0.73
-1950,2002,1996,extra2,0.67
-1950,2006,2000,logistic1,0.59
-1950,2006,1988,logistic2,0.67
-1950,2011,1978,rf1,0.83
-1950,2009,1980,logistic2,0.73
-1950,2005,1997,extra2,0.71
-1950,2003,2002,rf2,0.78
-1950,2004,1997,rf1,0.76
-1950,2006,1976,extra1,0.7
-1950,2008,1976,logistic1,0.68
-1950,2004,1984,rf2,0.79
-1950,2006,1974,decision1,0.5
-1950,2008,2001,rf2,0.81
-1950,2010,2005,decision2,0.6
-1950,2000,1983,extra1,0.74
-1950,2000,1986,logistic2,0.7
-1950,2002,1975,extra1,0.76
-1950,2001,1989,logistic1,0.84
-1950,2001,1986,rf2,0.76
-1950,2002,2002,extra2,0.71
-1950,2009,1977,logistic2,0.66
-1950,2004,2003,logistic2,0.72
-1950,2002,1990,rf1,0.78
-1950,2003,1999,rf2,0.75
-1950,2003,1990,extra2,0.79
-1950,2010,1996,logistic2,0.61
-1950,2008,1995,decision2,0.43
-1950,2010,1990,extra1,0.8
-1950,2003,1993,decision2,0.55
-1950,2001,1980,extra2,0.76
-1950,2010,1977,decision2,0.52
-1950,2002,1999,logistic2,0.56
-1950,2001,1983,rf2,0.83
-1950,2007,1995,extra1,0.7
-1950,2006,1997,rf2,0.78
-1950,2002,1980,logistic1,0.74
-1950,2010,1987,rf1,0.74
-1950,2009,2005,logistic2,0.59
-1950,2010,1993,decision1,0.7
-1950,2010,1980,rf2,0.78
-1950,2007,1979,extra2,0.76
-1950,2000,1992,rf2,0.76
-1950,2001,1996,rf1,0.67
-1950,2009,1989,rf2,0.87
-1950,2007,2001,logistic2,0.73
-1950,2002,1977,logistic1,0.67
-1950,2007,2004,extra2,0.77
-1950,2011,1999,logistic1,0.62
-1950,2007,1988,logistic1,0.67
-1950,2000,1980,extra1,0.71
-1950,2006,2004,rf1,0.78
-1950,2003,1996,logistic1,0.58
-1950,2011,1981,extra1,0.83
-1950,2007,1998,logistic2,0.58
-1950,2004,1994,rf1,0.76
-1950,2009,1983,extra2,0.79
-1950,2009,2002,decision1,0.6
-1950,2007,1992,rf1,0.76
-1950,2005,2000,rf2,0.75
-1950,2004,1978,extra2,0.79
-1950,2008,1998,decision2,0.54
-1950,2003,1987,logistic2,0.63
-1950,2010,2008,rf2,0.82
-1950,2006,1979,decision1,0.67
-1950,2003,1984,logistic2,0.69
-1950,2006,1982,logistic2,0.77
-1950,2005,1988,decision1,0.68
-1950,2011,1990,extra2,0.84
-1950,2007,1976,extra2,0.73
-1950,2005,2003,rf2,0.85
-1950,2008,1992,extra2,0.72
-1950,2000,1989,decision2,0.64
-1950,2001,1993,rf1,0.72
-1950,2003,1981,extra1,0.84
-1950,2002,1993,logistic2,0.75
-1950,2005,1978,logistic1,0.74
-1950,2011,1987,logistic2,0.67
-1950,2007,1982,decision2,0.64
-1950,2002,1987,rf1,0.73
-1950,2005,1985,decision1,0.56
-1950,2000,1977,extra1,0.68
-1950,2010,1974,rf2,0.81
-1950,2004,1981,decision2,0.71
-1950,2008,1989,logistic2,0.82
-1950,2004,1987,rf2,0.74
-1950,2009,1986,decision2,0.57
-1950,2006,1991,decision2,0.58
-1950,2011,1975,rf1,0.79
-1950,2001,1983,logistic1,0.7
-1950,2001,1986,logistic1,0.71
-1950,2003,1978,extra1,0.7
-1950,2009,1992,logistic1,0.76
-1950,2003,2002,logistic1,0.61
-1950,2000,1999,rf1,0.7
-1950,2008,2001,logistic1,0.74
-1950,2008,1986,decision1,0.64
-1950,2003,1999,logistic1,0.6
-1950,2004,1984,logistic1,0.64
-1950,2007,1985,logistic1,0.61
-1950,2010,1973,logistic2,0.62
-1950,2000,1983,decision1,0.6
-1950,2008,1973,rf2,0.82
-1950,2001,1977,decision2,0.6
-1950,2005,1975,rf2,0.76
-1950,2010,1999,extra2,0.7
-1950,2009,2008,extra2,0.79
-1950,2000,1980,decision1,0.69
-1950,2007,1974,rf1,0.76
-1950,2007,2007,decision2,0.68
-1950,2000,1992,logistic1,0.78
-1950,2008,1983,extra1,0.76
-1950,2006,1985,extra2,0.65
-1950,2011,1996,rf2,0.78
-1950,2004,1975,extra1,0.78
-1950,2002,1984,rf1,0.66
-1950,2001,1999,extra1,0.7
-1950,2008,1980,rf1,0.74
-1950,2002,1975,decision1,0.64
-1950,2006,1997,logistic1,0.63
-1950,2007,1995,decision1,0.43
-1950,2006,1994,rf2,0.77
-1950,2009,1999,extra1,0.67
-1950,2004,2000,extra1,0.76
-1950,2008,2008,rf1,0.82
-1950,2005,1991,extra2,0.9
-1950,2011,1993,decision2,0.58
-1950,2009,1996,rf1,0.67
-1950,2011,2003,rf1,0.84
-1950,2004,1991,rf1,0.77
-1950,2003,1975,rf1,0.74
-1950,2005,1994,extra2,0.76
-1950,2000,1986,extra2,0.78
-1950,2001,1996,extra1,0.64
-1950,2009,1989,logistic1,0.83
-1950,2003,1993,rf2,0.8
-1950,2000,1977,decision1,0.58
-1950,2004,2003,extra2,0.86
-1950,2006,1974,logistic2,0.65
-1950,2005,2000,logistic1,0.6
-1950,2010,1984,rf1,0.7
-1950,2002,1996,decision2,0.45
-1950,2006,1988,extra2,0.82
-1950,2009,1974,extra1,0.77
-1950,2005,1981,rf2,0.84
-1950,2001,1990,rf1,0.79
-1950,2010,2002,decision2,0.55
-1950,2005,2003,logistic1,0.74
-1950,2002,2002,decision2,0.53
-1950,2000,1995,decision2,0.41
-1950,2002,1981,rf1,0.86
-1950,2006,1976,decision1,0.68
-1950,2002,1999,extra2,0.69
-1950,2003,1981,decision1,0.76
-1950,2011,1981,decision1,0.68
-1950,2000,1980,logistic2,0.69
-1950,2008,2005,rf1,0.75
-1950,2004,1994,extra1,0.76
-1950,2004,1997,extra1,0.68
-1950,2001,1980,decision2,0.53
-1950,2010,2005,rf2,0.81
-1950,2002,1990,extra1,0.76
-1950,2007,1998,extra2,0.63
-1950,2010,1996,extra2,0.69
-1950,2005,1985,logistic2,0.62
-1950,2008,1995,rf2,0.75
-1950,2006,1979,logistic2,0.71
-1950,2000,1989,rf2,0.88
-1950,2011,2006,extra1,0.72
-1950,2004,1987,logistic1,0.7
-1950,2000,1999,extra1,0.67
-1950,2006,2001,rf1,0.86
-1950,2002,1978,rf1,0.74
-1950,2009,1980,extra2,0.77
-1950,2010,1990,decision1,0.68
-1950,2003,1997,rf1,0.77
-1950,2011,1973,extra1,0.74
-1950,2000,1983,logistic2,0.67
-1950,2005,1975,logistic1,0.73
-1950,2008,1973,logistic1,0.68
-1950,2006,1994,logistic1,0.77
-1950,2007,1992,extra1,0.72
-1950,2003,1987,extra2,0.71
-1950,2010,2008,logistic1,0.65
-1950,2011,1978,extra1,0.79
-1950,2001,1993,extra1,0.74
-1950,2011,1984,logistic2,0.61
-1950,2006,2004,extra1,0.81
-1950,2010,1980,logistic1,0.75
-1950,2010,1993,logistic2,0.75
-1950,2004,2000,decision1,0.63
-1950,2005,1988,logistic2,0.7
-1950,2004,1975,decision1,0.61
-1950,2010,1974,logistic1,0.71
-1950,2002,1993,extra2,0.8
-1950,2002,1975,logistic2,0.72
-1950,2009,1977,extra2,0.74
-1950,2003,1984,extra2,0.69
-1950,2001,1999,decision1,0.58
-1950,2008,1977,rf1,0.77
-1950,2010,1977,rf2,0.77
-1950,2007,2001,extra2,0.81
-1950,2010,1987,extra1,0.7
-1950,2001,1977,rf2,0.74
-1950,2008,1998,rf2,0.73
-1950,2007,1989,rf1,0.83
-1950,2002,1987,extra1,0.68
-1950,2006,1991,rf2,0.86
-1950,2003,1978,decision1,0.56
-1950,2001,1984,rf1,0.67
-1950,2009,2005,extra2,0.79
-1950,2001,1987,rf1,0.69
-1950,2006,1982,extra2,0.79
-1950,2001,1996,decision1,0.63
-1950,2004,1978,decision2,0.59
-1950,2003,1993,logistic1,0.76
-1950,2003,2003,rf1,0.84
-1950,2008,1983,decision1,0.7
-1950,2007,1982,rf2,0.79
-1950,2002,1990,decision1,0.71
-1950,2009,2002,logistic2,0.56
-1950,2007,1995,logistic2,0.64
-1950,2003,1990,decision2,0.63
-1950,2004,1981,rf2,0.86
-1950,2004,1985,rf1,0.63
-1950,2004,1997,decision1,0.61
-1950,2009,1999,decision1,0.61
-1950,2011,2009,decision1,0.6
-1950,2007,1976,decision2,0.48
-1950,2005,1979,rf1,0.69
-1950,2005,1981,logistic1,0.81
-1950,2000,1995,rf2,0.77
-1950,2011,1996,logistic1,0.59
-1950,2007,2004,decision2,0.68
-1950,2003,2000,rf1,0.78
-1950,2008,2002,rf1,0.68
-1950,2011,1987,extra2,0.78
-1950,2008,1992,decision2,0.73
-1950,2008,1989,extra2,0.87
-1950,2008,1986,logistic2,0.71
-1950,2006,1985,decision2,0.54
-1950,2007,2007,rf2,0.82
-1950,2002,1984,extra1,0.66
-1950,2009,1986,rf2,0.73
-1950,2000,1986,decision2,0.6
-1950,2005,1997,decision2,0.61
-1950,2009,1974,decision1,0.62
-1950,2004,1994,decision1,0.56
-1950,2001,1993,decision1,0.62
-1950,2007,1979,decision2,0.52
-1950,2002,1996,rf2,0.76
-1950,2011,2000,rf1,0.8
-1950,2010,1973,extra2,0.77
-1950,2001,1990,extra1,0.78
-1950,2006,1974,extra2,0.8
-1950,2003,1981,logistic2,0.8
-1950,2006,1976,logistic2,0.66
-1950,2007,1974,extra1,0.78
-1950,2009,1983,decision2,0.55
-1950,2007,1986,rf1,0.75
-1950,2006,1998,rf1,0.65
-1950,2011,1975,extra1,0.8
-1950,2004,1991,extra1,0.84
-1950,2006,2004,decision1,0.69
-1950,2005,1994,decision2,0.57
-1950,2000,1983,extra2,0.8
-1950,2005,1985,extra2,0.65
-1950,2010,2005,logistic1,0.63
-1950,2001,1980,rf2,0.78
-1950,2008,1995,logistic1,0.75
-1950,2008,1980,extra1,0.74
-1950,2011,1973,decision1,0.55
-1950,2002,1999,decision2,0.57
-1950,2002,1987,decision1,0.62
-1950,2003,1975,extra1,0.78
-1950,2009,1993,rf1,0.72
-1950,2004,2000,logistic2,0.62
-1950,2002,2002,rf2,0.75
-1950,2002,1981,extra1,0.82
-1950,2011,2003,extra1,0.8
-1950,2001,1977,logistic1,0.68
-1950,2005,1991,decision2,0.58
-1950,2009,2008,decision2,0.71
-1950,2011,2006,decision1,0.68
-1950,2005,2001,rf1,0.85
-1950,2002,1978,extra1,0.75
-1950,2000,1995,logistic1,0.66
-1950,2012,1981,rf2,0.82
-1950,2009,1990,rf1,0.75
-1950,2002,1984,decision1,0.64
-1950,2001,1996,logistic2,0.58
-1950,2008,2008,extra1,0.74
-1950,2005,2004,rf1,0.77
-1950,2004,1988,rf1,0.7
-1950,2011,1990,decision2,0.61
-1950,2004,2003,decision2,0.62
-1950,2007,1992,decision1,0.79
-1950,2010,1977,logistic1,0.7
-1950,2010,2002,rf2,0.79
-1950,2006,1991,logistic1,0.83
-1950,2004,1981,logistic1,0.79
-1950,2003,1978,logistic2,0.68
-1950,2010,1990,logistic2,0.78
-1950,2011,1993,rf2,0.8
-1950,2009,1996,extra1,0.67
-1950,2011,1978,decision1,0.62
-1950,2004,1975,logistic2,0.72
-1950,2002,1996,logistic1,0.57
-1950,2008,1998,logistic1,0.59
-1950,2012,1984,logistic1,0.66
-1950,2012,1973,rf1,0.79
-1950,2006,1979,extra2,0.75
-1950,2002,1990,logistic2,0.76
-1950,2003,1997,extra1,0.68
-1950,2001,1999,logistic2,0.59
-1950,2001,1990,decision1,0.71
-1950,2007,1982,logistic1,0.76
-1950,2008,2005,extra1,0.74
-1950,2003,1987,decision2,0.6
-1950,2002,1993,decision2,0.61
-1950,2011,1981,logistic2,0.81
-1950,2007,2007,logistic1,0.71
-1950,2001,1987,extra1,0.7
-1950,2006,2001,extra1,0.78
-1950,2010,1984,extra1,0.64
-1950,2005,1976,rf1,0.68
-1950,2003,2003,extra1,0.84
-1950,2007,1998,decision2,0.59
-1950,2012,1975,extra2,0.8
-1950,2003,1984,decision2,0.69
-1950,2006,1995,rf1,0.73
-1950,2010,1987,decision1,0.64
-1950,2005,1988,extra2,0.8
-1950,2007,1989,extra1,0.83
-1950,2002,2002,logistic1,0.58
-1950,2009,1986,logistic1,0.73
-1950,2001,1984,extra1,0.67
-1950,2004,1978,rf2,0.82
-1950,2004,1997,logistic2,0.63
-1950,2002,1981,decision1,0.71
-1950,2004,1994,logistic2,0.76
-1950,2006,1988,decision2,0.57
-1950,2009,1980,decision2,0.52
-1950,2003,1990,rf2,0.81
-1950,2010,1999,decision2,0.68
-1950,2001,1993,logistic2,0.74
-1950,2011,1984,extra2,0.71
-1950,2001,1980,logistic1,0.73
-1950,2005,1997,rf2,0.77
-1950,2010,1975,rf1,0.81
-1950,2002,1978,decision1,0.63
-1950,2005,1979,extra1,0.69
-1950,2003,1994,rf1,0.77
-1950,2010,2009,rf1,0.73
-1950,2003,1997,decision1,0.62
-1950,2008,1974,rf1,0.77
-1950,2009,2002,extra2,0.71
-1950,2004,1991,decision1,0.69
-1950,2007,1976,rf2,0.74
-1950,2010,1993,extra2,0.79
-1950,2007,1974,decision1,0.55
-1950,2003,2000,extra1,0.73
-1950,2011,1975,decision1,0.7
-1950,2008,1992,rf2,0.81
-1950,2004,1985,extra1,0.56
-1950,2008,1980,decision1,0.67
-1950,2006,1985,rf2,0.7
-1950,2008,1983,logistic2,0.68
-1950,2003,1981,extra2,0.84
-1950,2008,1977,extra1,0.72
-1950,2010,1981,rf1,0.83
-1950,2001,1996,extra2,0.66
-1950,2010,1996,decision2,0.53
-1950,2012,1978,decision2,0.58
-1950,2003,1975,decision1,0.63
-1950,2007,1995,extra2,0.69
-1950,2009,1999,logistic2,0.64
-1950,2007,2004,rf2,0.78
-1950,2002,1987,logistic2,0.63
-1950,2001,1987,decision1,0.56
-1950,2009,1996,decision1,0.6
-1950,2001,1990,logistic2,0.78
-1950,2010,2002,logistic1,0.63
-1950,2011,2009,logistic2,0.62
-1950,2007,1979,rf2,0.77
-1950,2002,1999,rf2,0.74
-1950,2008,2002,extra1,0.64
-1950,2011,2003,decision1,0.61
-1950,2006,2001,decision1,0.65
-1950,2005,1982,rf1,0.82
-1950,2009,1977,decision2,0.51
-1950,2007,1989,decision1,0.81
-1950,2008,2008,decision1,0.67
-1950,2005,1994,rf2,0.78
-1950,2003,2003,decision1,0.6
-1950,2006,1976,extra2,0.72
-1950,2009,1974,logistic2,0.69
-1950,2008,1986,extra2,0.73
-1950,2006,1982,decision2,0.61
-1950,2006,2004,logistic2,0.68
-1950,2007,2001,decision2,0.61
-1950,2004,2000,extra2,0.82
-1950,2005,1985,decision2,0.56
-1950,2011,1997,rf1,0.73
-1950,2012,1981,logistic1,0.85
-1950,2006,1974,decision2,0.56
-1950,2000,1996,rf1,0.69
-1950,2002,1984,logistic2,0.68
-1950,2008,1989,decision2,0.7
-1950,2011,1987,decision2,0.49
-1950,2004,1978,logistic1,0.75
-1950,2011,1973,logistic2,0.7
-1950,2009,2005,decision2,0.61
-1950,2006,1998,extra1,0.64
-1950,2004,2003,rf2,0.85
-1950,2005,1991,rf2,0.85
-1950,2010,1973,decision2,0.53
-1950,2007,1976,logistic1,0.68
-1950,2001,1999,extra2,0.71
-1950,2002,1990,extra2,0.78
-1950,2010,2006,rf1,0.77
-1950,2008,2005,decision1,0.52
-1950,2003,1978,extra2,0.78
-1950,2002,1978,logistic2,0.67
-1950,2004,1975,extra2,0.76
-1950,2012,1994,decision1,0.56
-1950,2005,1997,logistic1,0.64
-1950,2008,1996,rf1,0.68
-1950,2002,1993,rf2,0.77
-1950,2003,1987,rf2,0.75
-1950,2005,2001,extra1,0.83
-1950,2002,1997,rf1,0.75
-1950,2005,1988,decision2,0.58
-1950,2010,1990,extra2,0.83
-1950,2010,1984,decision1,0.66
-1950,2004,1982,rf1,0.81
-1950,2003,2000,decision1,0.63
-1950,2011,1993,logistic1,0.75
-1950,2003,1990,logistic1,0.77
-1950,2004,1988,extra1,0.68
-1950,2007,1986,extra1,0.8
-1950,2004,1985,decision1,0.59
-1950,2005,2004,extra1,0.82
-1950,2002,1981,logistic2,0.75
-1950,2009,1983,rf2,0.82
-1950,2009,1993,extra1,0.76
-1950,2007,1992,logistic2,0.77
-1950,2012,1991,extra1,0.88
-1950,2011,2000,extra1,0.78
-1950,2011,1990,rf2,0.86
-1950,2009,2008,rf2,0.86
-1950,2007,1979,logistic1,0.7
-1950,2006,1985,logistic1,0.62
-1950,2002,1999,logistic1,0.56
-1950,2003,1997,logistic2,0.62
-1950,2008,1977,decision1,0.64
-1950,2004,1991,logistic2,0.81
-1950,2003,1984,rf2,0.77
-1950,2008,2002,decision1,0.58
-1950,2010,1978,rf1,0.82
-1950,2011,2006,logistic2,0.66
-1950,2006,1992,rf1,0.77
-1950,2001,1993,extra2,0.79
-1950,2012,1988,rf1,0.76
-1950,2001,1996,decision2,0.52
-1950,2005,1979,decision1,0.48
-1950,2003,1994,extra1,0.77
-1950,2005,1976,extra1,0.67
-1950,2002,1987,extra2,0.7
-1950,2008,1999,rf1,0.73
-1950,2011,1981,extra2,0.76
-1950,2009,1990,extra1,0.78
-1950,2001,1981,rf1,0.85
-1950,2004,1997,extra2,0.74
-1950,2012,1997,logistic2,0.65
-1950,2007,1998,rf2,0.69
-1950,2004,1994,extra2,0.78
-1950,2003,1981,decision2,0.66
-1950,2007,1983,rf1,0.77
-1950,2009,1987,rf1,0.73
-1950,2010,1999,rf2,0.8
-1950,2005,1994,logistic1,0.78
-1950,2007,1974,logistic2,0.69
-1950,2012,1973,extra1,0.74
-1950,2007,2004,logistic1,0.71
-1950,2003,1975,logistic2,0.73
-1950,2007,1989,logistic2,0.83
-1950,2010,1987,logistic2,0.66
-1950,2006,1995,extra1,0.7
-1950,2005,1991,logistic1,0.83
-1950,2006,2001,logistic2,0.73
-1950,2003,2003,logistic2,0.69
-1950,2008,1992,logistic1,0.77
-1950,2004,2003,logistic1,0.73
-1950,2012,1985,rf1,0.63
-1950,2002,1993,logistic1,0.75
-1950,2012,1975,decision2,0.62
-1950,2011,1978,logistic2,0.71
-1950,2006,1988,rf2,0.8
-1950,2005,2001,decision1,0.66
-1950,2010,2009,extra1,0.78
-1950,2009,1980,rf2,0.78
-1950,2005,1985,rf2,0.69
-1950,2012,2000,extra2,0.77
-1950,2008,1974,extra1,0.8
-1950,2010,1975,extra1,0.81
-1950,2007,1995,decision2,0.41
-1950,2003,1987,logistic1,0.7
-1950,2006,1979,decision2,0.55
-1950,2002,1978,extra2,0.79
-1950,2004,1988,decision1,0.56
-1950,2009,1996,logistic2,0.61
-1950,2003,1984,logistic1,0.64
-1950,2003,2000,logistic2,0.57
-1950,2006,1998,decision1,0.55
-1950,2008,1983,extra2,0.77
-1950,2012,1978,rf2,0.79
-1950,2002,1984,extra2,0.71
-1950,2010,1996,rf2,0.77
-1950,2005,1982,extra1,0.76
-1950,2007,2001,rf2,0.82
-1950,2009,1977,rf2,0.78
-1950,2003,1978,decision2,0.6
-1950,2009,2002,decision2,0.55
-1950,2008,2008,logistic2,0.62
-1950,2009,1999,extra2,0.72
-1950,2012,2009,logistic1,0.67
-1950,2006,2004,extra2,0.77
-1950,2012,2006,rf2,0.89
-1950,2005,1976,decision1,0.69
-1950,2009,2008,logistic1,0.66
-1950,2009,1974,extra2,0.79
-1950,2003,1994,decision1,0.69
-1950,2010,1981,extra1,0.83
-1950,2002,1981,extra2,0.84
-1950,2005,2004,decision1,0.69
-1950,2004,1985,logistic2,0.61
-1950,2007,1986,decision1,0.74
-1950,2012,1991,decision1,0.77
-1950,2012,2003,decision2,0.69
-1950,2004,1979,rf1,0.69
-1950,2001,1999,decision2,0.57
-1950,2006,1982,rf2,0.75
-1950,2011,1984,decision2,0.62
-1950,2011,2009,extra2,0.79
-1950,2011,1990,logistic1,0.77
-1950,2003,1997,extra2,0.71
-1950,2002,1990,decision2,0.64
-1950,2009,1983,logistic1,0.71
-1950,2007,1998,logistic1,0.59
-1950,2006,1974,rf2,0.8
-1950,2002,1997,extra1,0.68
-1950,2004,1982,extra1,0.76
-1950,2009,1993,decision1,0.67
-1950,2011,1975,logistic2,0.73
-1950,2012,1982,rf1,0.79
-1950,2006,1995,decision1,0.47
-1950,2008,1977,logistic2,0.68
-1950,2011,2000,decision1,0.58
-1950,2004,2000,decision2,0.57
-1950,2009,2005,rf2,0.83
-1950,2011,1973,extra2,0.78
-1950,2011,2003,logistic2,0.73
-1950,2003,1991,rf1,0.78
-1950,2005,1985,logistic1,0.62
-1950,2012,1973,decision1,0.65
-1950,2005,1998,rf1,0.68
-1950,2002,2000,rf1,0.77
-1950,2007,1977,rf1,0.72
-1950,2011,1997,extra1,0.7
-1950,2008,1974,decision1,0.61
-1950,2008,1989,rf2,0.89
-1950,2010,1999,logistic1,0.63
-1950,2006,1988,logistic1,0.66
-1950,2010,2003,rf1,0.86
-1950,2010,1984,logistic2,0.65
-1950,2008,2005,logistic2,0.56
-1950,2004,1991,extra2,0.9
-1950,2005,1988,rf2,0.76
-1950,2009,1980,logistic1,0.74
-1950,2009,1990,decision1,0.67
-1950,2003,1981,rf2,0.83
-1950,2010,1993,decision2,0.6
-1950,2005,1979,logistic2,0.69
-1950,2011,1987,rf2,0.79
-1950,2003,1975,extra2,0.76
-1950,2004,1994,decision2,0.61
-1950,2006,1976,decision2,0.61
-1950,2010,1973,rf2,0.81
-1950,2009,1977,logistic1,0.71
-1950,2012,1994,logistic2,0.75
-1950,2010,1990,decision2,0.62
-1950,2004,1997,decision2,0.6
-1950,2010,1975,decision1,0.67
-1950,2007,1989,extra2,0.88
-1950,2008,1996,extra1,0.65
-1950,2008,1980,logistic2,0.71
-1950,2006,1986,rf1,0.75
-1950,2010,2009,decision1,0.59
-1950,2008,1986,decision2,0.57
-1950,2005,2001,logistic2,0.75
-1950,2007,1992,extra2,0.69
-1950,2007,2001,logistic1,0.75
-1950,2010,2006,extra1,0.71
-1950,2008,2002,logistic2,0.56
-1950,2006,1992,extra1,0.75
-1950,2003,2003,extra2,0.86
-1950,2012,1978,logistic1,0.74
-1950,2004,1975,decision2,0.51
-1950,2004,1982,decision1,0.66
-1950,2009,1987,extra1,0.73
-1950,2010,1996,logistic1,0.58
-1950,2010,1981,decision1,0.82
-1950,2005,1982,decision1,0.65
-1950,2002,1997,decision1,0.66
-1950,2002,1994,rf1,0.77
-1950,2007,1980,rf1,0.76
-1950,2004,1988,logistic2,0.63
-1950,2010,1978,extra1,0.79
-1950,2007,1974,extra2,0.77
-1950,2005,1995,rf1,0.71
-1950,2012,2006,logistic1,0.68
-1950,2003,1988,rf1,0.72
-1950,2003,1985,rf1,0.62
-1950,2006,2001,extra2,0.8
-1950,2007,1983,extra1,0.74
-1950,2008,1999,extra1,0.66
-1950,2003,1994,logistic2,0.75
-1950,2005,2004,logistic2,0.65
-1950,2003,2000,extra2,0.81
-1950,2005,1992,rf1,0.75
-1950,2007,1995,rf2,0.77
-1950,2005,1988,logistic1,0.66
-1950,2006,1998,logistic2,0.58
-1950,2006,1982,logistic1,0.76
-1950,2011,1994,rf1,0.75
-1950,2004,2004,rf1,0.77
-1950,2003,1981,logistic1,0.79
-1950,2005,1976,logistic2,0.66
-1950,2007,2005,rf1,0.75
-1950,2006,1979,rf2,0.79
-1950,2011,2006,extra2,0.78
-1950,2002,1984,decision2,0.62
-1950,2001,1999,rf2,0.72
-1950,2002,1987,decision2,0.53
-1950,2006,1995,logistic2,0.63
-1950,2003,1978,rf2,0.83
-1950,2008,1993,rf1,0.77
-1950,2012,1975,rf2,0.82
-1950,2012,1997,extra2,0.76
-1950,2004,1985,extra2,0.65
-1950,2010,1987,extra2,0.72
-1950,2002,1990,rf2,0.78
-1950,2008,1989,logistic1,0.83
-1950,2008,2008,extra2,0.77
-1950,2009,2005,logistic1,0.62
-1950,2011,1981,decision2,0.7
-1950,2012,1988,extra1,0.78
-1950,2007,1986,logistic2,0.71
-1950,2011,1978,extra2,0.79
-1950,2012,1985,extra1,0.59
-1950,2004,1979,extra1,0.69
-1950,2009,1996,extra2,0.68
-1950,2011,1987,logistic1,0.7
-1950,2009,2002,rf2,0.73
-1950,2010,1973,logistic1,0.69
-1950,2002,1981,decision2,0.58
-1950,2002,2000,extra1,0.76
-1950,2008,1996,decision1,0.51
-1950,2009,1993,logistic2,0.74
-1950,2004,2000,rf2,0.76
-1950,2008,1983,decision2,0.56
-1950,2003,1997,decision2,0.63
-1950,2008,1977,extra2,0.75
-1950,2005,1979,extra2,0.79
-1950,2003,1975,decision2,0.5
-1950,2005,1986,rf1,0.75
-1950,2005,1982,logistic2,0.76
-1950,2004,1997,rf2,0.78
-1950,2003,1991,extra1,0.86
-1950,2012,1982,extra1,0.76
-1950,2004,1982,logistic2,0.76
-1950,2011,1997,decision1,0.69
-1950,2010,2006,decision1,0.61
-1950,2005,1998,extra1,0.65
-1950,2012,1991,logistic2,0.8
-1950,2009,1999,decision2,0.6
-1950,2010,2003,extra1,0.81
-1950,2008,1974,logistic2,0.67
-1950,2004,1994,rf2,0.76
-1950,2006,1992,decision1,0.77
-1950,2006,1976,rf2,0.74
-1950,2007,1977,extra1,0.71
-1950,2012,2000,decision2,0.61
-1950,2006,1989,rf1,0.85
-1950,2010,1984,extra2,0.71
-1950,2002,1990,logistic1,0.78
-1950,2009,1987,decision1,0.58
-1950,2011,1975,extra2,0.82
-1950,2011,2000,logistic2,0.61
-1950,2012,2003,rf2,0.84
-1950,2009,2009,rf1,0.76
-1950,2012,1973,logistic2,0.71
-1950,2007,1983,decision1,0.64
-1950,2004,1979,decision1,0.55
-1950,2011,1984,rf2,0.79
-1950,2009,1981,rf1,0.84
-1950,2007,1999,rf1,0.71
-1950,2003,1978,logistic1,0.74
-1950,2008,2005,extra2,0.78
-1950,2011,2003,extra2,0.82
-1950,2003,2003,decision2,0.71
-1950,2004,1988,extra2,0.8
-1950,2011,1973,decision2,0.58
-1950,2010,2000,rf1,0.79
-1950,2005,2001,extra2,0.84
-1950,2002,2000,decision1,0.56
-1950,2009,1984,rf1,0.66
-1950,2008,1999,decision1,0.68
-1950,2006,2004,decision2,0.71
-1950,2008,1986,rf2,0.74
-1950,2012,2010,rf1,0.8
-1950,2010,1981,logistic2,0.79
-1950,2009,1974,decision2,0.57
-1950,2004,1975,rf2,0.79
-1950,2004,1991,decision2,0.59
-1950,2011,1991,rf1,0.84
-1950,2010,1978,decision1,0.7
-1950,2003,1994,extra2,0.76
-1950,2007,1995,logistic1,0.75
-1950,2006,1979,logistic1,0.69
-1950,2011,2009,decision2,0.57
-1950,2009,1978,rf1,0.79
-1950,2008,1980,extra2,0.74
-1950,2002,1994,extra1,0.76
-1950,2012,1994,extra2,0.77
-1950,2004,2000,logistic1,0.6
-1950,2006,1986,extra1,0.8
-1950,2010,1990,rf2,0.84
-1950,2010,2009,logistic2,0.64
-1950,2010,1993,rf2,0.79
-1950,2005,2004,extra2,0.76
-1950,2009,1990,logistic2,0.77
-1950,2005,1995,extra1,0.7
-1950,2002,1987,rf2,0.72
-1950,2002,1984,rf2,0.75
-1950,2008,2002,extra2,0.71
-1950,2012,1988,decision1,0.7
-1950,2003,1988,extra1,0.71
-1950,2012,1975,logistic1,0.71
-1950,2012,1985,decision1,0.52
-1950,2005,1976,extra2,0.72
-1950,2003,1985,extra1,0.6
-1950,2003,1991,decision1,0.64
-1950,2005,1989,rf1,0.83
-1950,2007,1989,decision2,0.69
-1950,2007,2002,rf1,0.67
-1950,2006,1998,extra2,0.61
-1950,2005,1992,extra1,0.75
-1950,2003,1982,rf1,0.79
-1950,2006,2001,decision2,0.64
-1950,2005,1998,decision1,0.58
-1950,2003,1997,rf2,0.77
-1950,2004,2004,extra1,0.81
-1950,2007,1980,extra1,0.73
-1950,2007,1986,extra2,0.77
-1950,2007,1992,decision2,0.75
-1950,2006,1995,extra2,0.71
-1950,2006,1976,logistic1,0.67
-1950,2010,1975,logistic2,0.72
-1950,2012,1982,decision1,0.67
-1950,2004,1997,logistic1,0.64
-1950,2008,2008,decision2,0.7
-1950,2004,1994,logistic1,0.77
-1950,2009,2002,logistic1,0.63
-1950,2010,1997,rf1,0.75
-1950,2003,2000,decision2,0.65
-1950,2007,2005,extra1,0.73
-1950,2009,1993,extra2,0.78
-1950,2007,1974,decision2,0.41
-1950,2005,1982,extra2,0.79
-1950,2003,1975,rf2,0.79
-1950,2007,1977,decision1,0.59
-1950,2004,1985,decision2,0.56
-1950,2010,2003,decision1,0.64
-1950,2011,1981,rf2,0.79
-1950,2005,1986,extra1,0.81
-1950,2006,1983,rf1,0.77
-1950,2008,1993,extra1,0.72
-1950,2004,1988,decision2,0.61
-1950,2010,1974,rf1,0.79
-1950,2011,1994,extra1,0.79
-1950,2004,1975,logistic1,0.72
-1950,2005,1992,decision1,0.72
-1950,2011,1984,logistic1,0.65
-1950,2012,1979,rf1,0.76
-1950,2006,1986,decision1,0.73
-1950,2011,1988,rf1,0.77
-1950,2004,1982,extra2,0.76
-1950,2009,2006,rf1,0.74
-1950,2002,2000,logistic2,0.6
-1950,2002,1987,logistic1,0.71
-1950,2008,1990,rf1,0.76
-1950,2004,1979,logistic2,0.71
-1950,2006,1992,logistic2,0.78
-1950,2004,1991,rf2,0.86
-1950,2003,1985,decision1,0.64
-1950,2012,2007,rf1,0.82
-1950,2008,1983,rf2,0.79
-1950,2008,1986,logistic1,0.72
-1950,2010,2006,logistic2,0.69
-1950,2003,2003,rf2,0.88
-1950,2008,1996,logistic2,0.6
-1950,2003,1988,decision1,0.57
-1950,2002,1991,rf1,0.77
-1950,2005,1995,decision1,0.51
-1950,2009,1996,decision2,0.58
-1950,2008,1977,decision2,0.54
-1950,2012,1997,decision2,0.61
-1950,2009,1987,logistic2,0.67
-1950,2012,1991,extra2,0.89
-1950,2003,1997,logistic1,0.66
-1950,2005,2001,decision2,0.6
-1950,2012,2003,logistic1,0.75
-1950,2007,1983,logistic2,0.7
-1950,2011,2006,decision2,0.65
-1950,2004,2004,decision1,0.67
-1950,2008,1974,extra2,0.77
-1950,2010,1990,logistic1,0.77
-1950,2006,1989,extra1,0.85
-1950,2012,1973,extra2,0.78
-1950,2008,1999,logistic2,0.63
-1950,2009,1999,rf2,0.81
-1950,2010,1993,logistic1,0.77
-1950,2007,1980,decision1,0.63
-1950,2010,1987,decision2,0.51
-1950,2003,1979,rf1,0.7
-1950,2006,2004,rf2,0.76
-1950,2007,1999,extra1,0.68
-1950,2007,2005,decision1,0.76
-1950,2005,1979,decision2,0.6
-1950,2003,1991,logistic2,0.81
-1950,2009,1974,rf2,0.82
-1950,2010,1981,extra2,0.76
-1950,2005,1986,decision1,0.68
-1950,2005,1989,extra1,0.86
-1950,2010,1978,logistic2,0.72
-1950,2005,1998,logistic2,0.59
-1950,2012,2000,rf2,0.75
-1950,2003,1994,decision2,0.56
-1950,2005,2004,decision2,0.6
-1950,2008,1993,decision1,0.65
-1950,2011,2000,extra2,0.77
-1950,2010,2000,extra1,0.76
-1950,2009,2009,extra1,0.77
-1950,2011,1978,decision2,0.58
-1950,2009,1981,extra1,0.84
-1950,2006,2001,rf2,0.83
-1950,2011,1975,decision2,0.63
-1950,2006,1980,rf1,0.74
-1950,2011,1991,extra1,0.89
-1950,2004,2001,rf1,0.86
-1950,2011,1973,rf2,0.83
-1950,2004,1991,logistic1,0.83
-1950,2011,1994,decision1,0.55
-1950,2003,2000,rf2,0.75
-1950,2003,1975,logistic1,0.72
-1950,2007,1996,rf1,0.7
-1950,2011,1997,logistic2,0.65
-1950,2003,2003,logistic1,0.68
-1950,2006,1989,decision1,0.72
-1950,2007,1989,rf2,0.9
-1950,2003,1982,extra1,0.74
-1950,2008,2008,rf2,0.85
-1950,2009,1984,extra1,0.67
-1950,2012,1994,decision2,0.6
-1950,2004,1985,rf2,0.69
-1950,2009,1990,extra2,0.8
-1950,2010,2009,extra2,0.76
-1950,2007,1977,logistic2,0.7
-1950,2011,2009,rf2,0.81
-1950,2006,1977,rf1,0.73
-1950,2004,1988,rf2,0.77
-1950,2007,1992,rf2,0.76
-1950,2008,2005,decision2,0.68
-1950,2005,1989,decision1,0.8
-1950,2005,1992,logistic2,0.77
-1950,2011,1981,logistic1,0.84
-1950,2004,1998,rf1,0.63
-1950,2004,1995,rf1,0.7
-1950,2010,1984,decision2,0.63
-1950,2005,1976,decision2,0.62
-1950,2012,2010,extra1,0.75
-1950,2010,1975,extra2,0.79
-1950,2011,2003,decision2,0.67
-1950,2009,1978,extra1,0.78
-1950,2005,1982,decision2,0.46
-1950,2003,1985,logistic2,0.58
-1950,2006,1998,decision2,0.55
-1950,2003,1988,logistic2,0.66
-1950,2007,2002,extra1,0.66
-1950,2006,2004,logistic1,0.71
-1950,2012,1976,rf1,0.7
-1950,2006,1986,logistic2,0.7
-1950,2007,1986,decision2,0.61
-1950,2004,1982,decision2,0.6
-1950,2012,1988,logistic2,0.68
-1950,2005,1995,logistic2,0.65
-1950,2004,1979,extra2,0.75
-1950,2008,2002,decision2,0.54
-1950,2009,1993,decision2,0.65
-1950,2007,1999,decision1,0.59
-1950,2006,1983,extra1,0.76
-1950,2004,1976,rf1,0.69
-1950,2012,1985,logistic2,0.63
-1950,2010,1974,extra1,0.82
-1950,2007,1974,rf2,0.82
-1950,2010,1997,extra1,0.72
-1950,2010,2003,logistic2,0.74
-1950,2006,1992,extra2,0.71
-1950,2009,2003,rf1,0.86
-1950,2008,1983,logistic1,0.72
-1950,2004,1985,logistic1,0.62
-1950,2012,1979,extra1,0.75
-1950,2003,1991,extra2,0.9
-1950,2004,2004,logistic2,0.66
-1950,2005,2001,rf2,0.83
-1950,2006,2001,logistic1,0.74
-1950,2012,1982,logistic2,0.76
-1950,2004,1988,logistic1,0.66
-1950,2005,1986,logistic2,0.69
-1950,2009,2009,decision1,0.66
-1950,2011,1988,extra1,0.77
-1950,2011,1985,rf1,0.63
-1950,2005,1979,rf2,0.79
-1950,2009,1984,decision1,0.7
-1950,2009,1981,decision1,0.74
-1950,2008,1980,decision2,0.56
-1950,2009,2006,extra1,0.72
-1950,2007,1989,logistic1,0.83
-1950,2005,1998,extra2,0.63
-1950,2006,1995,decision2,0.44
-1950,2007,2005,logistic2,0.59
-1950,2009,1999,logistic1,0.63
-1950,2011,1991,decision1,0.67
-1950,2008,1990,extra1,0.78
-1950,2003,1982,decision1,0.55
-1950,2008,1996,extra2,0.69
-1950,2008,1977,rf2,0.77
-1950,2003,1979,extra1,0.7
-1950,2003,2000,logistic1,0.59
-1950,2007,1983,extra2,0.79
-1950,2003,1994,rf2,0.78
-1950,2008,1987,rf1,0.74
-1950,2009,1996,rf2,0.79
-1950,2012,1997,rf2,0.76
-1950,2010,2000,decision1,0.55
-1950,2008,2008,logistic1,0.67
-1950,2007,1992,logistic1,0.76
-1950,2012,2007,extra1,0.85
-1950,2008,1999,extra2,0.7
-1950,2010,2006,extra2,0.73
-1950,2012,2000,logistic1,0.64
-1950,2009,1987,extra2,0.76
-1950,2012,1973,decision2,0.52
-1950,2010,1981,decision2,0.7
-1950,2006,1989,logistic2,0.85
-1950,2007,1980,logistic2,0.71
-1950,2011,2006,rf2,0.86
-1950,2010,1987,rf2,0.79
-1950,2008,1974,decision2,0.49
-1950,2008,1993,logistic2,0.75
-1950,2010,1978,extra2,0.81
-1950,2007,1977,extra2,0.75
-1950,2012,2010,decision1,0.71
-1950,2009,1974,logistic1,0.73
-1950,2005,2004,rf2,0.77
-1950,2004,2001,extra1,0.8
-1950,2004,1998,extra1,0.64
-1950,2010,1991,rf1,0.82
-1950,2003,1976,rf1,0.68
-1950,2005,1992,extra2,0.71
-1950,2010,1997,decision1,0.62
-1950,2011,1973,logistic1,0.69
-1950,2006,1983,decision1,0.61
-1950,2011,2009,logistic1,0.67
-1950,2004,1992,rf1,0.76
-1950,2007,2002,decision1,0.59
-1950,2005,1976,rf2,0.73
-1950,2006,1977,extra1,0.69
-1950,2006,1980,extra1,0.74
-1950,2012,2004,rf1,0.73
-1950,2011,1994,logistic2,0.75
-1950,2005,1989,logistic2,0.84
-1950,2004,1995,extra1,0.71
-1950,2010,1994,rf1,0.74
-1950,2009,1978,decision1,0.65
-1950,2012,1991,decision2,0.62
-1950,2006,1986,extra2,0.74
-1950,2003,1988,extra2,0.79
-1950,2004,1982,rf2,0.75
-1950,2007,1974,logistic1,0.72
-1950,2011,1978,rf2,0.85
-1950,2007,1996,extra1,0.65
-1950,2005,1986,extra2,0.75
-1950,2009,2006,decision1,0.59
-1950,2011,1975,rf2,0.82
-1950,2005,2001,logistic1,0.75
-1950,2010,1984,rf2,0.77
-1950,2005,1995,extra2,0.73
-1950,2011,1997,extra2,0.71
-1950,2012,1994,rf2,0.81
-1950,2004,1986,rf1,0.74
-1950,2006,2005,rf1,0.74
-1950,2011,1988,decision1,0.64
-1950,2011,2000,decision2,0.68
-1950,2006,1998,rf2,0.7
-1950,2005,1982,rf2,0.77
-1950,2004,1979,decision2,0.48
-1950,2004,2004,extra2,0.78
-1950,2008,2005,rf2,0.83
-1950,2007,1999,logistic2,0.64
-1950,2009,1993,rf2,0.8
-1950,2004,1976,extra1,0.69
-1950,2004,1998,decision1,0.58
-1950,2003,1991,decision2,0.59
-1950,2008,1990,decision1,0.66
-1950,2011,1982,rf1,0.79
-1950,2012,1979,decision1,0.62
-1950,2007,1986,rf2,0.74
-1950,2006,1992,decision2,0.8
-1950,2010,2009,decision2,0.58
-1950,2011,2003,rf2,0.84
-1950,2004,2001,decision1,0.71
-1950,2008,1984,rf1,0.67
-1950,2012,1976,extra1,0.67
-1950,2008,2002,rf2,0.78
-1950,2010,1975,decision2,0.55
-1950,2009,1996,logistic1,0.57
-1950,2008,1977,logistic1,0.71
-1950,2005,1979,logistic1,0.69
-1950,2009,1990,decision2,0.66
-1950,2006,1977,decision1,0.54
-1950,2004,1989,rf1,0.83
-1950,2012,1985,extra2,0.71
-1950,2004,1995,decision1,0.4
-1950,2006,1995,rf2,0.77
-1950,2010,2003,extra2,0.85
-1950,2006,2002,rf1,0.67
-1950,2012,1982,extra2,0.8
-1950,2012,1988,extra2,0.83
-1950,2006,1989,extra2,0.87
-1950,2005,2004,logistic1,0.7
-1950,2006,1980,decision1,0.63
-1950,2012,2007,decision1,0.64
-1950,2003,2001,rf1,0.86
-1950,2009,2009,logistic2,0.64
-1950,2005,1976,logistic1,0.68
-1950,2010,1987,logistic1,0.69
-1950,2009,2003,extra1,0.78
-1950,2005,1992,decision2,0.75
-1950,2007,1996,decision1,0.64
-1950,2004,1992,extra1,0.76
-1950,2007,2005,extra2,0.79
-1950,2009,1984,logistic2,0.67
-1950,2006,1998,logistic1,0.58
-1950,2012,1994,logistic1,0.75
-1950,2006,1983,logistic2,0.71
-1950,2007,1993,rf1,0.75
-1950,2004,1982,logistic1,0.75
-1950,2004,1976,decision1,0.57
-1950,2011,1991,logistic2,0.82
-1950,2012,1997,logistic1,0.63
-1950,2008,1980,rf2,0.78
-1950,2005,1998,decision2,0.54
-1950,2007,1983,decision2,0.54
-1950,2011,1978,logistic1,0.74
-1950,2007,1990,rf1,0.77
-1950,2011,1985,extra1,0.59
-1950,2011,2006,logistic1,0.67
-1950,2009,1981,logistic2,0.8
-1950,2012,1973,rf2,0.79
-1950,2007,1980,extra2,0.75
-1950,2008,1974,rf2,0.83
-1950,2010,1981,rf2,0.81
-1950,2008,1999,decision2,0.6
-1950,2010,2006,decision2,0.62
-1950,2007,1977,decision2,0.51
-1950,2008,1993,extra2,0.76
-1950,2008,1987,extra1,0.73
-1950,2005,1986,decision2,0.64
-1950,2010,2000,logistic2,0.61
-1950,2009,1987,decision2,0.53
-1950,2009,1975,rf1,0.8
-1950,2008,2005,logistic1,0.6
-1950,2009,2000,rf1,0.8
-1950,2007,2002,logistic2,0.58
-1950,2010,1997,logistic2,0.65
-1950,2008,1996,decision2,0.58
-1950,2004,1998,logistic2,0.56
-1950,2004,1979,rf2,0.79
-1950,2005,1989,extra2,0.87
-1950,2011,1994,extra2,0.76
-1950,2010,1978,decision2,0.57
-1950,2010,1991,extra1,0.89
-1950,2005,1995,decision2,0.41
-1950,2006,1986,decision2,0.61
-1950,2007,1986,logistic1,0.73
-1950,2010,1984,logistic1,0.66
-1950,2004,2001,logistic2,0.68
-1950,2005,1982,logistic1,0.75
-1950,2004,1992,decision1,0.72
-1950,2012,1976,decision1,0.7
-1950,2011,2010,rf1,0.77
-1950,2011,2003,logistic1,0.74
-1950,2006,1995,logistic1,0.73
-1950,2009,1978,logistic2,0.7
-1950,2005,2002,rf1,0.69
-1950,2011,1974,rf1,0.8
-1950,2006,2005,extra1,0.67
-1950,2009,1993,logistic1,0.76
-1950,2012,2001,rf1,0.85
-1950,2009,2006,logistic2,0.63
-1950,2007,1999,extra2,0.69
-1950,2004,2004,decision2,0.67
-1950,2008,2002,logistic1,0.61
-1950,2006,1977,logistic2,0.68
-1950,2006,1992,rf2,0.75
-1950,2007,1975,rf1,0.79
-1950,2010,1994,extra1,0.77
-1950,2012,2004,extra1,0.79
-1950,2008,1990,logistic2,0.78
-1950,2011,1985,decision1,0.55
-1950,2009,2003,decision1,0.69
-1950,2004,1989,extra1,0.83
-1950,2010,1981,logistic1,0.84
-1950,2005,1980,rf1,0.73
-1950,2004,1995,logistic2,0.63
-1950,2011,1988,logistic2,0.7
-1950,2008,1987,decision1,0.69
-1950,2008,1980,logistic1,0.74
-1950,2005,1998,rf2,0.72
-1950,2010,1975,rf2,0.83
-1950,2012,1991,rf2,0.9
-1950,2008,1984,extra1,0.64
-1950,2011,2000,rf2,0.74
-1950,2004,1976,logistic2,0.63
-1950,2006,1980,logistic2,0.73
-1950,2008,1978,rf1,0.79
-1950,2006,2002,extra1,0.63
-1950,2012,2010,logistic2,0.68
-1950,2007,1996,logistic2,0.61
-1950,2011,1975,logistic1,0.72
-1950,2008,1974,logistic1,0.73
-1950,2005,1992,rf2,0.77
-1950,2009,1990,rf2,0.82
-1950,2006,1983,extra2,0.77
-1950,2005,2005,rf1,0.77
-1950,2009,1984,extra2,0.75
-1950,2010,2009,rf2,0.78
-1950,2007,1990,extra1,0.78
-1950,2012,1979,logistic2,0.73
-1950,2011,1997,decision2,0.59
-1950,2011,1982,extra1,0.77
-1950,2006,1989,decision2,0.68
-1950,2005,1977,rf1,0.71
-1950,2006,1999,rf1,0.7
-1950,2007,1983,rf2,0.81
-1950,2009,1997,rf1,0.75
-1950,2006,2005,decision1,0.55
-1950,2010,1991,decision1,0.78
-1950,2007,1993,extra1,0.72
-1950,2007,1977,rf2,0.79
-1950,2004,1998,extra2,0.61
-1950,2012,1985,decision2,0.53
-1950,2010,1988,rf1,0.77
-1950,2008,1999,rf2,0.81
-1950,2009,2009,extra2,0.77
-1950,2010,2000,extra2,0.75
-1950,2011,1991,extra2,0.9
-1950,2012,1982,decision2,0.63
-1950,2012,2007,logistic2,0.71
-1950,2006,1992,logistic1,0.77
-1950,2009,1981,extra2,0.76
-1950,2005,1995,rf2,0.76
-1950,2012,1995,rf1,0.72
-1950,2004,2001,extra2,0.83
-1950,2012,1988,decision2,0.56
-1950,2008,1996,rf2,0.77
-1950,2007,2005,decision2,0.62
-1950,2007,2002,extra2,0.7
-1950,2010,1978,rf2,0.8
-1950,2009,1987,rf2,0.79
-1950,2011,1979,rf1,0.78
-1950,2008,1993,decision2,0.58
-1950,2006,1986,rf2,0.73
-1950,2009,1978,extra2,0.81
-1950,2012,1998,rf1,0.68
-1950,2012,1973,logistic1,0.7
-1950,2010,2006,rf2,0.85
-1950,2011,2000,logistic1,0.63
-1950,2010,1997,extra2,0.74
-1950,2010,1994,decision1,0.64
-1950,2009,1975,extra1,0.79
-1950,2005,1998,logistic1,0.58
-1950,2008,1984,decision1,0.65
-1950,2004,2004,rf2,0.8
-1950,2005,1983,rf1,0.78
-1950,2006,2002,decision1,0.55
-1950,2007,1980,decision2,0.61
-1950,2007,1977,logistic1,0.7
-1950,2011,2007,rf1,0.83
-1950,2006,1996,rf1,0.68
-1950,2005,2002,extra1,0.63
-1950,2010,2003,decision2,0.73
-1950,2007,1987,rf1,0.72
-1950,2008,2006,rf1,0.73
-1950,2006,1977,extra2,0.75
-1950,2011,1974,extra1,0.79
-1950,2005,1992,logistic1,0.77
-1950,2010,1975,logistic1,0.73
-1950,2009,2000,extra1,0.75
-1950,2010,1985,rf1,0.63
-1950,2005,1980,extra1,0.74
-1950,2007,1993,decision1,0.71
-1950,2007,1990,decision1,0.69
-1950,2009,1994,rf1,0.73
-1950,2007,1999,decision2,0.57
-1950,2005,1989,decision2,0.68
-1950,2011,1982,decision1,0.63
-1950,2007,1996,extra2,0.68
-1950,2012,1976,logistic2,0.68
-1950,2012,2004,decision1,0.75
-1950,2007,1975,extra1,0.78
-1950,2009,2006,extra2,0.76
-1950,2009,1990,logistic1,0.78
-1950,2004,1976,extra2,0.71
-1950,2011,2010,extra1,0.74
-1950,2008,1987,logistic2,0.68
-1950,2010,2009,logistic1,0.68
-1950,2008,1999,logistic1,0.61
-1950,2011,2004,rf1,0.75
-1950,2008,1990,extra2,0.83
-1950,2008,2003,rf1,0.85
-1950,2007,1983,logistic1,0.73
-1950,2012,1991,logistic1,0.8
-1950,2010,1982,rf1,0.82
-1950,2009,2003,logistic2,0.73
-1950,2006,1980,extra2,0.76
-1950,2008,1978,extra1,0.78
-1950,2012,2001,extra1,0.8
-1950,2005,1977,extra1,0.72
-1950,2011,1994,decision2,0.59
-1950,2005,2005,extra1,0.68
-1950,2008,1996,logistic1,0.56
-1950,2009,1987,logistic1,0.68
-1950,2004,2001,decision2,0.69
-1950,2011,1988,extra2,0.83
-1950,2006,1999,extra1,0.65
-1950,2011,1985,logistic2,0.61
-1950,2006,1989,rf2,0.91
-1950,2006,1986,logistic1,0.75
-1950,2006,1993,rf1,0.74
-1950,2005,2002,decision1,0.59
-1950,2009,1984,decision2,0.64
-1950,2009,1975,decision1,0.6
-1950,2005,1995,logistic1,0.73
-1950,2005,1980,decision1,0.63
-1950,2011,1976,rf1,0.72
-1950,2011,1997,rf2,0.76
-1950,2008,1981,rf1,0.83
-1950,2009,1978,decision2,0.53
-1950,2012,1982,rf2,0.77
-1950,2010,1988,extra1,0.78
-1950,2011,1974,decision1,0.5
-1950,2006,1983,decision2,0.57
-1950,2008,1975,rf1,0.79
-1950,2007,2005,rf2,0.82
-1950,2010,1978,logistic1,0.73
-1950,2010,1991,logistic2,0.82
-1950,2009,2009,decision2,0.62
-1950,2012,1979,extra2,0.77
-1950,2006,2005,logistic2,0.6
-1950,2009,2000,decision1,0.65
-1950,2012,2010,extra2,0.81
-1950,2008,1984,logistic2,0.66
-1950,2010,2006,logistic1,0.67
-1950,2012,1985,rf2,0.76
-1950,2008,1993,rf2,0.79
-1950,2009,1997,extra1,0.71
-1950,2007,2002,decision2,0.55
-1950,2006,2002,logistic2,0.57
-1950,2012,1995,extra1,0.72
-1950,2005,2005,decision1,0.59
-1950,2011,1991,decision2,0.54
-1950,2007,1975,decision1,0.73
-1950,2010,2000,decision2,0.67
-1950,2005,1977,decision1,0.58
-1950,2012,1988,rf2,0.85
-1950,2005,1983,extra1,0.76
-1950,2011,1979,extra1,0.72
-1950,2009,1981,decision2,0.62
-1950,2008,1978,decision1,0.65
-1950,2005,1999,rf1,0.71
-1950,2007,1990,logistic2,0.8
-1950,2012,2007,extra2,0.79
-1950,2006,1996,extra1,0.62
-1950,2005,1989,rf2,0.9
-1950,2007,1999,rf2,0.77
-1950,2007,1980,rf2,0.76
-1950,2007,1993,logistic2,0.76
-1950,2006,1989,logistic1,0.83
-1950,2007,1978,rf1,0.79
-1950,2012,1974,rf1,0.83
-1950,2011,2001,rf1,0.86
-1950,2012,2001,decision1,0.64
-1950,2006,1977,decision2,0.52
-1950,2008,1987,extra2,0.72
-1950,2007,1987,extra1,0.7
-1950,2008,2006,extra1,0.73
-1950,2010,1997,decision2,0.55
-1950,2010,1985,extra1,0.6
-1950,2010,2003,rf2,0.88
-1950,2010,1976,rf1,0.7
-1950,2011,2010,decision1,0.64
-1950,2012,1998,extra1,0.62
-1950,2011,1982,logistic2,0.76
-1950,2004,1976,decision2,0.53
-1950,2009,1994,extra1,0.8
-1950,2006,1980,decision2,0.52
-1950,2007,1984,rf1,0.68
-1950,2011,2007,extra1,0.84
-1950,2006,1999,decision1,0.57
-1950,2007,2005,logistic1,0.6
-1950,2010,1988,decision1,0.62
-1950,2008,2003,extra1,0.79
-1950,2010,1982,extra1,0.79
-1950,2007,1996,decision2,0.53
-1950,2009,2003,extra2,0.87
-1950,2006,1987,rf1,0.72
-1950,2010,2010,rf1,0.77
-1950,2005,1980,logistic2,0.7
-1950,2008,1990,decision2,0.6
-1950,2005,1983,decision1,0.64
-1950,2012,1985,logistic1,0.58
-1950,2012,1976,extra2,0.72
-1950,2008,2000,rf1,0.8
-1950,2012,1995,decision1,0.58
-1950,2011,1994,rf2,0.81
-1950,2012,1982,logistic1,0.75
-1950,2009,1988,rf1,0.76
-1950,2006,1983,rf2,0.83
-1950,2011,1979,decision1,0.5
-1950,2009,1997,decision1,0.69
-1950,2011,2004,extra1,0.79
-1950,2010,1994,logistic2,0.73
-1950,2009,1991,rf1,0.8
-1950,2012,2004,logistic2,0.75
-1950,2006,1993,extra1,0.74
-1950,2008,1993,logistic1,0.74
-1950,2008,1997,rf1,0.76
-1950,2009,1975,logistic2,0.7
-1950,2008,1984,extra2,0.71
-1950,2005,2005,logistic2,0.58
-1950,2009,1984,rf2,0.77
-1950,2011,1997,logistic1,0.64
-1950,2009,2006,decision2,0.62
-1950,2005,1996,rf1,0.67
-1950,2007,1987,decision1,0.57
-1950,2008,2006,decision1,0.59
-1950,2007,1999,logistic1,0.61
-1950,2006,1996,decision1,0.59
-1950,2006,2005,extra2,0.79
-1950,2007,1975,logistic2,0.72
-1950,2007,1980,logistic1,0.74
-1950,2009,2009,rf2,0.79
-1950,2008,1975,extra1,0.82
-1950,2011,1976,extra1,0.7
-1950,2011,1985,extra2,0.71
-1950,2012,1992,rf1,0.78
-1950,2008,1978,logistic2,0.68
-1950,2007,1990,extra2,0.8
-1950,2012,1988,logistic1,0.7
-1950,2009,1978,rf2,0.81
-1950,2006,2002,extra2,0.71
-1950,2007,2002,rf2,0.75
-1950,2009,2000,logistic2,0.6
-1950,2008,1981,extra1,0.88
-1950,2011,2007,decision1,0.61
-1950,2010,1979,rf1,0.77
-1950,2010,1985,decision1,0.57
-1950,2006,1993,decision1,0.54
-1950,2009,1994,decision1,0.7
-1950,2010,1991,extra2,0.9
-1950,2008,2003,decision1,0.74
-1950,2011,1988,decision2,0.64
-1950,2012,1998,decision1,0.52
-1950,2007,1993,extra2,0.77
-1950,2010,2000,rf2,0.71
-1950,2010,1982,decision1,0.6
-1950,2010,2003,logistic1,0.75
-1950,2012,2010,decision2,0.6
-1950,2011,1991,rf2,0.89
-1950,2009,1981,rf2,0.81
-1950,2006,1977,rf2,0.79
-1950,2005,1999,extra1,0.67
-1950,2010,2007,rf1,0.83
-1950,2011,2004,decision1,0.69
-1950,2011,1974,logistic2,0.69
-1950,2007,1978,extra1,0.76
-1950,2008,1987,decision2,0.57
-1950,2006,1990,rf1,0.78
-1950,2009,1984,logistic1,0.65
-1950,2012,1974,extra1,0.83
-1950,2006,1987,extra1,0.73
-1950,2006,1983,logistic1,0.72
-1950,2006,1999,logistic2,0.62
-1950,2010,1997,rf2,0.78
-1950,2006,1980,rf2,0.76
-1950,2011,1994,logistic1,0.75
-1950,2010,1976,extra1,0.68
-1950,2007,1996,rf2,0.76
-1950,2012,1979,decision2,0.62
-1950,2012,2001,logistic2,0.71
-1950,2008,1975,decision1,0.67
-1950,2008,1984,decision2,0.64
-1950,2008,1990,rf2,0.82
-1950,2011,2010,logistic2,0.64
-1950,2011,2001,extra1,0.76
-1950,2007,1984,extra1,0.65
-1950,2006,2002,decision2,0.54
-1950,2011,1982,extra2,0.8
-1950,2007,2006,rf1,0.75
-1950,2009,2003,decision2,0.64
-1950,2008,1981,decision1,0.75
-1950,2009,1997,logistic2,0.66
-1950,2007,1990,decision2,0.71
-1950,2009,2009,logistic1,0.65
-1950,2007,1978,decision1,0.67
-1950,2009,1975,extra2,0.8
-1950,2011,1976,decision1,0.66
-1950,2007,1987,logistic2,0.68
-1950,2009,1991,extra1,0.88
-1950,2005,1983,logistic2,0.7
-1950,2009,1988,extra1,0.77
-1950,2008,1994,rf1,0.74
-1950,2010,1988,logistic2,0.7
-1950,2009,1978,logistic1,0.73
-1950,2010,1994,extra2,0.77
-1950,2006,1996,logistic2,0.6
-1950,2008,2000,extra1,0.75
-1950,2008,2006,logistic2,0.6
-1950,2008,1978,extra2,0.8
-1950,2011,1979,logistic2,0.7
-1950,2008,1997,extra1,0.69
-1950,2012,1986,rf1,0.73
-1950,2012,1995,logistic2,0.72
-1950,2007,2002,logistic1,0.61
-1950,2012,2007,decision2,0.67
-1950,2010,2010,extra1,0.75
-1950,2012,1976,decision2,0.57
-1950,2007,1996,logistic1,0.55
-1950,2006,2005,decision2,0.65
-1950,2006,1987,decision1,0.53
-1950,2010,1976,decision1,0.64
-1950,2011,1991,logistic1,0.81
-1950,2010,2000,logistic1,0.6
-1950,2007,2000,rf1,0.79
-1950,2006,1993,logistic2,0.75
-1950,2009,1994,logistic2,0.73
-1950,2012,2004,extra2,0.74
-1950,2009,1981,logistic1,0.83
-1950,2012,1983,rf1,0.83
-1950,2011,1998,rf1,0.66
-1950,2007,1993,decision2,0.57
-1950,2012,1992,extra1,0.73
-1950,2006,1977,logistic1,0.69
-1950,2010,1979,extra1,0.76
-1950,2009,2006,rf2,0.86
-1950,2012,1974,decision1,0.6
-1950,2007,1981,rf1,0.82
-1950,2007,1984,decision1,0.56
-1950,2010,1997,logistic1,0.65
-1950,2010,1982,logistic2,0.77
-1950,2011,2001,decision1,0.59
-1950,2008,2003,logistic2,0.74
-1950,2009,2000,extra2,0.76
-1950,2008,1997,decision1,0.66
-1950,2012,1989,rf1,0.84
-1950,2011,1985,decision2,0.55
-1950,2009,1991,decision1,0.69
-1950,2008,1987,rf2,0.76
-1950,2011,1988,rf2,0.85
-1950,2010,1991,decision2,0.55
-1950,2010,2007,extra1,0.83
-1950,2006,1980,logistic1,0.75
-1950,2008,2000,decision1,0.6
-1950,2008,1990,logistic1,0.79
-1950,2009,1985,rf1,0.62
-1950,2006,1990,extra1,0.76
-1950,2010,1985,logistic2,0.63
-1950,2008,1975,logistic2,0.7
-1950,2006,1999,extra2,0.7
-1950,2012,1998,logistic2,0.6
-1950,2011,2007,logistic2,0.72
-1950,2012,2010,rf2,0.79
-1950,2008,1984,rf2,0.75
-1950,2006,1984,rf1,0.68
-1950,2011,1974,extra2,0.78
-1950,2006,2002,rf2,0.76
-1950,2009,1988,decision1,0.59
-1950,2012,1992,decision1,0.72
-1950,2010,2010,decision1,0.6
-1950,2011,2010,extra2,0.81
-1950,2010,2004,rf1,0.76
-1950,2007,1990,rf2,0.83
-1950,2007,1978,logistic2,0.72
-1950,2006,1996,extra2,0.7
-1950,2009,2003,rf2,0.87
-1950,2005,1983,extra2,0.8
-1950,2011,2004,logistic2,0.75
-1950,2010,1979,decision1,0.55
-1950,2008,1981,logistic2,0.8
-1950,2009,1979,rf1,0.72
-1950,2009,1997,extra2,0.72
-1950,2012,2001,extra2,0.81
-1950,2007,1997,rf1,0.76
-1950,2007,2006,extra1,0.7
-1950,2011,1976,logistic2,0.68
-1950,2009,1975,decision2,0.56
-1950,2006,2005,rf2,0.83
-1950,2008,1994,extra1,0.79
-1950,2007,1987,extra2,0.72
-1950,2012,1979,rf2,0.84
-1950,2009,1994,extra2,0.77
-1950,2006,1993,extra2,0.8
-1950,2010,2007,decision1,0.68
-1950,2012,1986,extra1,0.76
-1950,2011,1982,decision2,0.56
-1950,2006,1990,decision1,0.76
-1950,2008,1987,logistic1,0.67
-1950,2009,2006,logistic1,0.66
-1950,2011,1995,rf1,0.74
-1950,2012,2007,rf2,0.87
-1950,2008,2006,extra2,0.76
-1950,2007,2003,rf1,0.85
-1950,2010,1994,decision2,0.58
-1950,2010,1988,extra2,0.83
-1950,2008,1978,decision2,0.59
-1950,2007,1984,logistic2,0.68
-1950,2012,1976,rf2,0.8
-1950,2012,1995,extra2,0.71
-1950,2007,2000,extra1,0.73
-1950,2010,2001,rf1,0.86
-1950,2007,1993,rf2,0.8
-1950,2007,1981,extra1,0.85
-1950,2009,1982,rf1,0.8
-1950,2010,1976,logistic2,0.64
-1950,2006,2002,logistic1,0.6
-1950,2011,1979,extra2,0.77
-1950,2007,1990,logistic1,0.79
-1950,2010,1982,extra2,0.79
-1950,2008,1984,logistic1,0.65
-1950,2011,1988,logistic1,0.69
-1950,2009,2003,logistic1,0.76
-1950,2011,1998,extra1,0.62
-1950,2012,2004,decision2,0.69
-1950,2010,1985,extra2,0.7
-1950,2008,2003,extra2,0.87
-1950,2009,2000,decision2,0.62
-1950,2012,1974,logistic2,0.68
-1950,2011,1985,rf2,0.75
-1950,2010,1998,rf1,0.65
-1950,2008,1997,logistic2,0.64
-1950,2006,1981,rf1,0.82
-1950,2006,1999,decision2,0.64
-1950,2009,1991,logistic2,0.82
-1950,2011,1992,rf1,0.78
-1950,2008,2000,logistic2,0.61
-1950,2012,1983,extra1,0.81
-1950,2008,1975,extra2,0.8
-1950,2008,1994,decision1,0.78
-1950,2011,2001,logistic2,0.75
-1950,2012,1989,extra1,0.88
-1950,2007,2006,decision1,0.62
-1950,2006,1996,decision2,0.54
-1950,2010,1991,rf2,0.91
-1950,2011,2007,extra2,0.79
-1950,2012,2010,logistic1,0.68
-1950,2007,1987,decision2,0.61
-1950,2006,2005,logistic1,0.57
-1950,2012,1986,decision1,0.61
-1950,2011,1974,decision2,0.53
-1950,2007,1978,extra2,0.82
-1950,2007,1981,decision1,0.71
-1950,2006,1984,extra1,0.65
-1950,2007,2000,decision1,0.69
-1950,2011,2010,decision2,0.54
-1950,2012,1979,logistic1,0.75
-1950,2008,1991,rf1,0.78
-1950,2009,1985,extra1,0.59
-1950,2012,2007,logistic1,0.74
-1950,2009,1988,logistic2,0.71
-1950,2010,2010,logistic2,0.57
-1950,2012,1998,extra2,0.66
-1950,2008,1981,extra2,0.75
-1950,2009,1997,decision2,0.65
-1950,2006,1990,logistic2,0.75
-1950,2011,2004,extra2,0.74
-1950,2012,1992,logistic2,0.75
-1950,2010,1979,logistic2,0.69
-1950,2007,1997,extra1,0.68
-1950,2009,1979,extra1,0.75
-1950,2007,1993,logistic1,0.73
-1950,2010,2004,extra1,0.78
-1950,2010,2007,logistic2,0.7
-1950,2012,1983,decision1,0.57
-1950,2012,2001,decision2,0.66
-1950,2009,1975,rf2,0.81
-1950,2007,1984,extra2,0.72
-1950,2011,1976,extra2,0.73
-1950,2012,1976,logistic1,0.7
-1950,2008,1978,rf2,0.83
-1950,2008,1988,rf1,0.75
-1950,2011,1998,decision1,0.59
-1950,2011,1982,rf2,0.79
-1950,2010,1991,logistic1,0.81
-1950,2007,2003,extra1,0.79
-1950,2012,1989,decision1,0.74
-1950,2009,2007,rf1,0.84
-1950,2010,1994,rf2,0.78
-1950,2008,2006,decision2,0.55
-1950,2009,1994,decision2,0.61
-1950,2009,1982,extra1,0.76
-1950,2009,1985,decision1,0.58
-1950,2010,1988,decision2,0.58
-1950,2007,1991,rf1,0.78
-1950,2007,1997,decision1,0.61
-1950,2009,1991,extra2,0.9
-1950,2008,1985,rf1,0.65
-1950,2008,2000,extra2,0.79
-1950,2006,1999,rf2,0.8
-1950,2010,2001,extra1,0.77
-1950,2011,1995,extra1,0.73
-1950,2010,1976,extra2,0.73
-1950,2011,1979,decision2,0.5
-1950,2012,1995,decision2,0.46
-1950,2009,2004,rf1,0.76
-1950,2011,1989,rf1,0.85
-1950,2012,1974,extra2,0.77
-1950,2009,1979,decision1,0.64
-1950,2007,2006,logistic2,0.61
-1950,2008,1997,extra2,0.72
-1950,2009,2000,rf2,0.75
-1950,2008,2003,decision2,0.62
-1950,2008,1975,decision2,0.52
-1950,2010,2004,decision1,0.75
-1950,2010,1982,decision2,0.63
-1950,2010,1998,extra1,0.62
-1950,2007,2000,logistic2,0.6
-1950,2007,2003,decision1,0.75
-1950,2012,2004,rf2,0.76
-1950,2008,1994,logistic2,0.77
-1950,2006,2006,rf1,0.75
-1950,2011,2007,decision2,0.71
-1950,2008,1978,logistic1,0.75
-1950,2010,1994,logistic1,0.77
-1950,2011,1985,logistic1,0.57
-1950,2012,2011,rf1,0.74
-1950,2011,2010,rf2,0.78
-1950,2011,1992,extra1,0.74
-1950,2007,1987,rf2,0.8
-1950,2011,2001,extra2,0.84
-1950,2008,1981,decision2,0.68
-1950,2010,1985,decision2,0.56
-1950,2011,1974,rf2,0.82
-1950,2012,1986,logistic2,0.73
-1950,2007,1994,rf1,0.74
-1950,2011,1982,logistic1,0.76
-1950,2009,1982,decision1,0.68
-1950,2007,1981,logistic2,0.78
-1950,2008,1991,extra1,0.86
-1950,2009,1997,rf2,0.79
-1950,2009,1988,extra2,0.81
-1950,2011,2004,decision2,0.71
-1950,2007,1984,decision2,0.62
-1950,2012,1980,rf1,0.78
-1950,2010,2001,decision1,0.65
-1950,2010,2010,extra2,0.81
-1950,2012,1998,decision2,0.58
-1950,2012,1983,logistic2,0.73
-1950,2011,1995,decision1,0.46
-1950,2010,1979,extra2,0.74
-1950,2010,2007,extra2,0.78
-1950,2012,2008,rf1,0.82
-1950,2009,1975,logistic1,0.73
-1950,2008,1988,extra1,0.74
-1950,2010,1998,decision1,0.64
-1950,2012,1992,extra2,0.77
-1950,2010,1992,rf1,0.77
-1950,2009,2000,logistic1,0.59
-1950,2008,1985,extra1,0.58
-1950,2011,1992,decision1,0.77
-1950,2012,2001,rf2,0.84
-1950,2009,1991,decision2,0.6
-1950,2008,2006,rf2,0.84
-1950,2010,1976,decision2,0.58
-1950,2009,2007,extra1,0.82
-1950,2011,2010,logistic1,0.66
-1950,2011,1976,decision2,0.62
-1950,2011,1998,logistic2,0.6
-1950,2009,1994,rf2,0.77
-1950,2008,1997,decision2,0.58
-1950,2009,1985,logistic2,0.61
-1950,2010,1988,rf2,0.84
-1950,2007,1987,logistic1,0.67
-1950,2008,1991,decision1,0.72
-1950,2008,2000,decision2,0.58
-1950,2010,2004,logistic2,0.76
-1950,2010,1982,rf2,0.78
-1950,2007,2003,logistic2,0.72
-1950,2007,2006,extra2,0.75
-1950,2011,1979,rf2,0.81
-1950,2008,1994,extra2,0.76
-1950,2011,1989,extra1,0.87
-1950,2008,1988,decision1,0.76
-1950,2012,1977,rf1,0.79
-1950,2009,2004,extra1,0.79
-1950,2008,1985,decision1,0.6
-1950,2012,1989,logistic2,0.83
-1950,2008,2003,rf2,0.86
-1950,2012,2011,extra1,0.73
-1950,2009,1979,logistic2,0.71
-1950,2008,1979,rf1,0.79
-1950,2008,2006,logistic1,0.67
-1950,2008,1975,rf2,0.81
-1950,2009,2007,decision1,0.78
-1950,2012,1995,rf2,0.74
-1950,2009,1997,logistic1,0.65
-1950,2007,1994,extra1,0.78
-1950,2011,2001,decision2,0.66
-1950,2012,1986,extra2,0.74
-1950,2010,1995,rf1,0.74
-1950,2007,1981,extra2,0.76
-1950,2009,1982,logistic2,0.77
-1950,2011,1986,rf1,0.77
-1950,2010,1985,rf2,0.76
-1950,2009,1994,logistic1,0.77
-1950,2008,1981,rf2,0.78
-1950,2012,2004,logistic1,0.74
-1950,2011,2007,rf2,0.84
-1950,2012,1974,decision2,0.56
-1950,2007,1984,rf2,0.77
-1950,2010,1979,decision2,0.51
-1950,2011,1983,rf1,0.83
-1950,2009,1988,decision2,0.58
-1950,2010,2001,logistic2,0.76
-1950,2010,2010,decision2,0.59
-1950,2011,2004,rf2,0.75
-1950,2012,2001,logistic1,0.74
-1950,2012,1983,extra2,0.79
-1950,2012,1980,extra1,0.75
-1950,2010,2007,decision2,0.66
-1950,2012,1998,rf2,0.76
-1950,2012,2011,decision1,0.58
-1950,2008,1991,logistic2,0.81
-1950,2008,2003,logistic1,0.78
-1950,2011,1995,logistic2,0.65
-1950,2011,1989,decision1,0.76
-1950,2010,1998,logistic2,0.59
-1950,2009,2004,decision1,0.75
-1950,2007,2006,decision2,0.63
-1950,2012,2008,extra1,0.78
-1950,2011,1992,logistic2,0.77
-1950,2009,2001,rf1,0.86
-1950,2009,1976,rf1,0.7
-1950,2010,1982,logistic1,0.77
-1950,2008,1997,rf2,0.8
-1950,2011,1979,logistic1,0.74
-1950,2010,1992,extra1,0.71
-1950,2009,1985,extra2,0.67
-1950,2012,1992,decision2,0.75
-1950,2009,1991,rf2,0.88
-1950,2007,2003,extra2,0.86
-1950,2010,2004,extra2,0.77
-1950,2010,1988,logistic1,0.71
-1950,2008,1985,logistic2,0.6
-1950,2011,1998,extra2,0.69
-1950,2010,1985,logistic1,0.58
-1950,2008,1994,decision2,0.56
-1950,2009,1979,extra2,0.75
-1950,2008,2000,rf2,0.73
-1950,2010,1976,rf2,0.77
-1950,2011,1976,rf2,0.79
-1950,2011,2011,rf1,0.77
-1950,2007,1981,decision2,0.67
-1950,2008,1979,extra1,0.7
-1950,2009,2007,logistic2,0.7
-1950,2011,2007,logistic1,0.75
-1950,2008,1975,logistic1,0.73
-1950,2012,1986,decision2,0.62
-1950,2008,1988,logistic2,0.7
-1950,2009,1998,rf1,0.65
-1950,2008,2007,rf1,0.86
-1950,2012,2008,decision1,0.67
-1950,2008,1981,logistic1,0.83
-1950,2012,1989,extra2,0.9
-1950,2012,1980,decision1,0.72
-1950,2012,1977,extra1,0.74
-1950,2012,1974,rf2,0.83
-1950,2009,1995,rf1,0.74
-1950,2009,1982,extra2,0.8
-1950,2012,1983,decision2,0.59
-1950,2011,2004,logistic1,0.72
-1950,2010,1979,rf2,0.79
-1950,2010,1992,decision1,0.72
-1950,2009,1988,rf2,0.82
-1950,2011,2001,rf2,0.84
-1950,2010,1995,extra1,0.7
-1950,2012,1995,logistic1,0.75
-1950,2010,2001,extra2,0.81
-1950,2012,1998,logistic1,0.58
-1950,2008,1997,logistic1,0.66
-1950,2011,1986,extra1,0.77
-1950,2009,1991,logistic1,0.81
-1950,2010,2010,rf2,0.83
-1950,2012,2002,rf1,0.71
-1950,2008,1991,extra2,0.9
-1950,2010,2007,rf2,0.85
-1950,2009,2004,logistic2,0.74
-1950,2012,2005,rf1,0.79
-1950,2011,1983,extra1,0.83
-1950,2010,1983,rf1,0.83
-1950,2009,1985,decision2,0.55
-1950,2008,2004,rf1,0.78
-1950,2009,1976,extra1,0.69
-1950,2011,1980,rf1,0.79
-1950,2009,1979,decision2,0.55
-1950,2008,2000,logistic1,0.58
-1950,2010,1976,logistic1,0.69
-1950,2010,1998,extra2,0.71
-1950,2009,2001,extra1,0.79
-1950,2011,1995,extra2,0.68
-1950,2011,1992,extra2,0.7
-1950,2011,1986,decision1,0.61
-1950,2008,1994,rf2,0.78
-1950,2012,1977,decision1,0.51
-1950,2012,1992,rf2,0.78
-1950,2010,1986,rf1,0.76
-1950,2012,2011,logistic2,0.59
-1950,2011,1998,decision2,0.58
-1950,2010,2004,decision2,0.7
-1950,2009,2007,extra2,0.78
-1950,2010,1989,rf1,0.84
-1950,2010,1995,decision1,0.51
-1950,2008,1988,extra2,0.81
-1950,2011,1989,logistic2,0.83
-1950,2012,1986,rf2,0.78
-1950,2009,1998,extra1,0.64
-1950,2009,1995,extra1,0.71
-1950,2009,1988,logistic1,0.68
-1950,2011,1976,logistic1,0.7
-1950,2010,1979,logistic1,0.73
-1950,2009,1982,decision2,0.62
-1950,2008,2007,extra1,0.79
-1950,2011,2011,extra1,0.71
-1950,2011,1983,decision1,0.69
-1950,2012,1989,decision2,0.71
-1950,2011,2001,logistic1,0.74
-1950,2008,1976,rf1,0.71
-1950,2012,1983,rf2,0.84
-1950,2011,2008,rf1,0.83
-1950,2008,1982,rf1,0.81
-1950,2010,2001,decision2,0.67
-1950,2009,1976,decision1,0.62
-1950,2008,1991,decision2,0.65
-1950,2011,2005,rf1,0.79
-1950,2012,1974,logistic1,0.72
-1950,2009,2001,decision1,0.72
-1950,2010,1992,logistic2,0.78
-1950,2012,1980,logistic2,0.74
-1950,2009,2004,extra2,0.77
-1950,2009,1992,rf1,0.77
-1950,2012,2008,logistic2,0.67
-1950,2009,1998,decision1,0.66
-1950,2012,2002,extra1,0.66
-1950,2010,1998,decision2,0.63
-1950,2010,2007,logistic1,0.75
-1950,2012,1992,logistic1,0.77
-1950,2009,1995,decision1,0.37
-1950,2010,2010,logistic1,0.69
-1950,2010,1983,extra1,0.77
-1950,2011,1980,extra1,0.76
-1950,2011,1995,decision2,0.48
-1950,2008,2007,decision1,0.65
-1950,2008,2004,extra1,0.8
-1950,2009,1985,rf2,0.73
-1950,2009,1979,rf2,0.82
-1950,2011,2011,decision1,0.69
-1950,2012,1996,rf1,0.69
-1950,2009,2007,decision2,0.72
-1950,2012,1983,logistic1,0.73
-1950,2012,1986,logistic1,0.73
-1950,2010,1989,extra1,0.86
-1950,2010,1977,rf1,0.76
-1950,2011,1986,logistic2,0.71
-1950,2012,1999,rf1,0.73
-1950,2012,2005,extra1,0.72
-1950,2008,2001,rf1,0.85
-1950,2011,1992,decision2,0.74
-1950,2012,2011,extra2,0.72
-1950,2010,1995,logistic2,0.66
-1950,2012,1977,logistic2,0.71
-1950,2009,1976,logistic2,0.69
-1950,2010,1986,extra1,0.78
-1950,2011,1998,rf2,0.73
-1950,2010,2004,rf2,0.77
-1950,2010,1983,decision1,0.62
-1950,2010,1980,rf1,0.77
-1950,2012,2002,decision1,0.5
-1950,2011,1977,rf1,0.79
-1950,2011,1989,extra2,0.91
-1950,2009,1982,rf2,0.78
-1950,2010,2001,rf2,0.81
-1950,2011,1983,logistic2,0.71
-1950,2010,1992,extra2,0.75
-1950,2009,2001,logistic2,0.73
-1950,2009,1989,rf1,0.85
-1950,2012,1989,rf2,0.88
-1950,2010,1989,decision1,0.75
-1950,2011,1980,decision1,0.72
-1950,2012,2005,decision1,0.63
-1950,2009,2004,decision2,0.68
-1950,2011,2008,extra1,0.8
-1950,2011,2002,rf1,0.71
-1950,2008,1982,extra1,0.76
-1950,2010,1998,rf2,0.71
-1950,2012,1980,extra2,0.74
-1950,2011,2005,extra1,0.71
-1950,2010,2008,rf1,0.81
-1950,2009,1985,logistic1,0.58
-1950,2009,1992,extra1,0.74
-1950,2012,2008,extra2,0.84
-1950,2010,1986,decision1,0.61
-1950,2009,1998,logistic2,0.59
-1950,2010,2004,logistic1,0.72
-1950,2012,1975,rf1,0.78
-1950,2009,1995,logistic2,0.63
-1950,2011,1986,extra2,0.73
-1950,2009,2007,rf2,0.84
-1950,2012,1987,rf1,0.74
-1950,2012,1984,rf1,0.71
-1950,2009,1976,extra2,0.73
-1950,2011,1998,logistic1,0.58
-1950,2010,2001,logistic1,0.73
-1950,2012,2011,decision2,0.54
-1950,2009,1982,logistic1,0.77
-1950,2010,1977,extra1,0.72
-1950,2011,2005,decision1,0.64
-1950,2011,1995,rf2,0.73
-1950,2012,1996,extra1,0.68
-1950,2011,1992,rf2,0.8
-1950,2011,2008,decision1,0.71
-1950,2011,2011,logistic2,0.55
-1950,2012,1989,logistic1,0.85
-1950,2008,1982,decision1,0.58
-1950,2011,1989,decision2,0.71
-1950,2010,1995,extra2,0.71
-1950,2012,1993,rf1,0.74
-1950,2012,1999,extra1,0.68
-1950,2012,2002,logistic2,0.68
-1950,2009,1989,extra1,0.87
-1950,2010,1983,logistic2,0.67
-1950,2009,1992,decision1,0.78
-1950,2012,1977,extra2,0.76
-1950,2010,1980,extra1,0.75
-1950,2010,1998,logistic1,0.59
-1950,2010,1992,decision2,0.74
-1950,2010,1989,logistic2,0.84
-1950,2009,2004,rf2,0.8
-1950,2009,2001,extra2,0.82
-1950,2012,2008,decision2,0.74
-1950,2011,1977,extra1,0.76
-1950,2011,1983,extra2,0.78
-1950,2010,1977,decision1,0.54
-1950,2012,1980,decision2,0.59
-1950,2009,2007,logistic1,0.72
-1950,2011,1995,logistic1,0.76
-1950,2012,1996,decision1,0.55
-1950,2009,1986,rf1,0.76
-1950,2012,2005,logistic2,0.65
-1950,2010,2008,extra1,0.76
-1950,2011,2002,extra1,0.65
-1950,2011,1980,logistic2,0.74
-1950,2009,1976,decision2,0.64
-1950,2010,1980,decision1,0.59
-1950,2009,1998,extra2,0.67
-1950,2012,1975,extra1,0.8
-1950,2012,1999,decision1,0.62
-1950,2012,1984,extra1,0.69
-1950,2011,1986,decision2,0.58
-1950,2010,1995,decision2,0.47
-1950,2010,1986,logistic2,0.72
-1950,2010,2005,rf1,0.79
-1950,2012,2011,rf2,0.85
-1950,2011,1992,logistic1,0.76
-1950,2011,2008,logistic2,0.65
-1950,2010,2008,decision1,0.65
-1950,2010,2002,rf1,0.73
-1950,2011,2011,extra2,0.73
-1950,2011,2005,logistic2,0.64
-1950,2012,1987,extra1,0.71
-1950,2010,1983,extra2,0.78
-1950,2011,1977,decision1,0.6
-1950,2009,2001,decision2,0.62
-1950,2011,2002,decision1,0.51
-1950,2012,2002,extra2,0.75
-1950,2012,1990,rf1,0.78
-1950,2011,1989,rf2,0.89
-1950,2012,1977,decision2,0.58
-1950,2010,1989,extra2,0.88
-1950,2011,1999,rf1,0.74
-1950,2012,1984,decision1,0.7
-1950,2011,1983,decision2,0.55
-1950,2012,1993,extra1,0.76
-1950,2012,1975,decision1,0.57
-1950,2010,1999,rf1,0.74
-1950,2010,1992,rf2,0.81
-1950,2012,2008,rf2,0.82
-1950,2012,1996,logistic2,0.62
-1950,2010,1980,logistic2,0.7
-1950,2012,1980,rf2,0.83
-1950,2012,2005,extra2,0.79
-1950,2011,1980,extra2,0.79
-1950,2012,1987,decision1,0.68
-1950,2011,1996,rf1,0.7
-1950,2012,1993,decision1,0.79
-1950,2012,2011,logistic1,0.62
-1950,2011,1986,rf2,0.76
-1950,2012,1999,logistic2,0.64
-1950,2011,2008,extra2,0.83
-1950,2010,1986,extra2,0.74
-1950,2010,2005,extra1,0.75
-1950,2011,1989,logistic1,0.85
-1950,2010,1992,logistic1,0.76
-1950,2010,2002,extra1,0.66
-1950,2011,2011,decision2,0.63
-1950,2010,1995,rf2,0.76
-1950,2011,2005,extra2,0.82
-1950,2012,2002,decision2,0.54
-1950,2010,2008,logistic2,0.63
-1950,2011,1977,logistic2,0.68
-1950,2010,1989,decision2,0.68
-1950,2011,1993,rf1,0.77
-1950,2011,2002,logistic2,0.63
-1950,2012,1990,extra1,0.79
-1950,2011,1983,rf2,0.83
-1950,2012,1984,logistic2,0.63
-1950,2010,1999,extra1,0.68
-1950,2011,1999,extra1,0.69
-1950,2012,1980,logistic1,0.75
-1950,2012,2008,logistic1,0.69
-1950,2012,1975,logistic2,0.72
-1950,2010,2005,decision1,0.61
-1950,2012,1977,rf2,0.8
-1950,2010,2002,decision1,0.56
-1950,2012,1996,extra2,0.69
-1950,2012,2005,decision2,0.66
-1950,2011,1980,decision2,0.58
-1950,2010,1986,decision2,0.58
-1950,2011,1986,logistic1,0.73
-1950,2011,2008,decision2,0.75
-1950,2012,1993,logistic2,0.73
-1950,2012,1987,logistic2,0.67
-1950,2012,1990,decision1,0.72
-1950,2011,1999,decision1,0.57
-1950,2012,1999,extra2,0.74
-1950,2011,2005,decision2,0.67
-1950,2010,1995,logistic1,0.76
-1950,2011,1996,extra1,0.67
-1950,2011,1983,logistic1,0.73
-1950,2011,1977,extra2,0.73
-1950,2010,2008,extra2,0.83
-1950,2012,2012,rf1,0.79
-1950,2010,1989,rf2,0.9
-1950,2012,2002,rf2,0.77
-1950,2011,2011,rf2,0.86
-1950,2011,2002,extra2,0.72
-1950,2011,1993,extra1,0.76
-1950,2010,2005,logistic2,0.6
-1950,2011,1990,rf1,0.76
-1950,2012,1981,rf1,0.83
-1950,2012,1996,decision2,0.52
-1950,2011,1996,decision1,0.58
-1950,2012,1984,extra2,0.71
-1950,2011,1987,rf1,0.73
-1950,2012,2009,rf1,0.73
-1950,2012,1993,extra2,0.81
-1950,2012,1999,decision2,0.66
-1950,2011,1993,decision1,0.59
-1950,2012,1977,logistic1,0.73
-1950,2012,1990,logistic2,0.75
-1950,2011,2008,rf2,0.83
-1950,2012,1987,extra2,0.78
-1950,2011,1999,logistic2,0.64
-1950,2011,1977,decision2,0.55
-1950,2012,2005,rf2,0.83
-1950,2011,1980,rf2,0.78
-1950,2011,2011,logistic1,0.65
-1950,2012,2002,logistic1,0.65
-1950,2011,2005,rf2,0.83
-1950,2010,1996,rf1,0.69
-1950,2011,2002,decision2,0.58
-1950,2012,1984,decision2,0.66
-1950,2012,2012,extra1,0.7
-1950,2011,1996,logistic2,0.62
-1950,2011,1990,extra1,0.79
-1950,2012,1981,extra1,0.85
-1950,2011,1984,rf1,0.71
-1950,2011,1980,logistic1,0.74
-1950,2012,1993,decision2,0.62
-1950,2011,2008,logistic1,0.65
-1950,2011,1987,extra1,0.69
-1950,2012,1987,decision2,0.6
-1950,2012,1990,extra2,0.84
-1950,2011,1993,logistic2,0.72
-1950,2012,2005,logistic1,0.64
-1950,2011,2005,logistic1,0.64
-1950,2012,2009,extra1,0.8
-1950,2012,1996,rf2,0.77
-1950,2011,1990,decision1,0.67
-1950,2011,1999,extra2,0.73
-1950,2012,2012,decision1,0.63
-1950,2012,1981,decision1,0.74
-1950,2012,1978,rf1,0.81
-1950,2011,2002,rf2,0.76
-1950,2011,1987,decision1,0.71
-1950,2012,2003,rf1,0.83
-1950,2012,2009,decision1,0.65
-1950,2012,1984,rf2,0.81
-1950,2012,1999,rf2,0.82
-1950,2011,1977,rf2,0.79
-1950,2011,1996,extra2,0.7
-1950,2011,1984,extra1,0.66
-1950,2012,1996,logistic1,0.59
-1950,2012,1993,rf2,0.81
-1950,2011,2009,rf1,0.75
-1950,2011,2002,logistic1,0.62
-1950,2012,1990,decision2,0.65
-1950,2012,1987,rf2,0.8
-1950,2011,1993,extra2,0.81
-1950,2011,1999,decision2,0.62
-1950,2012,2012,logistic2,0.61
-1950,2011,1981,rf1,0.83
-1950,2011,1990,logistic2,0.77
-1950,2012,1981,logistic2,0.81
-1950,2011,2006,rf1,0.76
-1950,2012,1999,logistic1,0.62
-1950,2012,2006,rf1,0.73
-1950,2012,1993,logistic1,0.75
-1950,2011,1977,logistic1,0.73
-1950,2012,1978,extra1,0.81
-1950,2011,1996,decision2,0.51
-1950,2012,2009,logistic2,0.62
-1950,2012,1987,logistic1,0.7
-1950,2012,2003,extra1,0.77
-1950,2012,1978,decision1,0.65
-1950,2012,2003,decision1,0.7
-1950,2012,2012,extra2,0.76
-1950,2012,1981,extra2,0.75
-1950,2011,2009,extra1,0.78
-1950,2012,2006,extra1,0.69
-1950,2012,2009,extra2,0.74
-1950,2012,1990,rf2,0.85
-1950,2012,2006,decision1,0.72
-1950,2012,1981,decision2,0.63
-1950,2012,2012,decision2,0.56
-1950,2012,2003,logistic2,0.74
-1950,2012,1994,rf1,0.75
-1950,2012,1978,logistic2,0.74
-1950,2012,1997,rf1,0.76
-1950,2012,1990,logistic1,0.77
-1950,2012,2009,decision2,0.62
-1950,2012,2000,rf1,0.8
-1950,2011,1999,rf2,0.83
-1950,2012,2006,logistic2,0.66
-1950,2012,1994,extra1,0.77
-1950,2012,2000,extra1,0.79
-1950,2012,2003,extra2,0.83
-1950,2012,1991,rf1,0.83
-1950,2012,2012,rf2,0.77
-1950,2012,2006,extra2,0.82
-1950,2012,1978,extra2,0.79
-1950,2012,2000,decision1,0.53
-1950,2012,1997,extra1,0.71
-1950,2012,2012,logistic1,0.59
-1950,2012,2009,rf2,0.82
-1950,2012,2006,decision2,0.66
-1950,2012,1997,decision1,0.64
-1950,2012,2000,logistic2,0.59
diff --git a/curriculum/3_modeling_and_machine_learning/machine-learning/data_cv/get_cv_results_KFold.py b/curriculum/3_modeling_and_machine_learning/machine-learning/data_cv/get_cv_results_KFold.py
new file mode 100644
index 00000000..9fe40fc5
--- /dev/null
+++ b/curriculum/3_modeling_and_machine_learning/machine-learning/data_cv/get_cv_results_KFold.py
@@ -0,0 +1,189 @@
+from itertools import product
+import multiprocessing as mp
+import random
+import csv
+import timeit
+import math
+
+import numpy as np
+import pandas as pd
+from sklearn import metrics
+from sklearn.model_selection import KFold
+from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+
+
+def precision_at_abs(labels, predictions_proba):
+    cutoff_index = 100
+
+    random.seed(15)
+    predictions_proba_sorted, labels_sorted = zip(*sorted(
+        zip(predictions_proba, labels),
+        key=lambda pair: (pair[0], random.random()), reverse=True)
+    )
+
+    test_predictions_binary = [
+        1 if x < cutoff_index else 0
+        for x in range(len(predictions_proba_sorted))
+    ]
+
+    return metrics.precision_score(labels_sorted, test_predictions_binary)
+
+def standard_cv_classification(validation_year, model, fold, queue, indices):
+    start_time = timeit.default_timer()
+    try:
+        complete_data = data[data.SENTENCE_END_YEAR <= validation_year]
+
+        # prep validation data
+        validation_df = complete_data.iloc[indices[validation_year]['test'][fold]]
+        validation_X = validation_df[X_cols]
+        validation_y = validation_df[y_cols]
+
+        # prep training data
+        training_df = complete_data.iloc[indices[validation_year]['train'][fold]]
+        training_X = training_df[X_cols]
+        training_y = training_df[y_cols]
+
+        # fit the model
+        clf = clfs[model]
+        clf.fit(training_X, training_y)
+        index_of_class_1 = [i for i,val in enumerate(clf.classes_) if val == 1][0]
+
+        predictions = clf.predict_proba(validation_X)[:, index_of_class_1]
+        validation_precision = precision_at_abs(validation_y, predictions)
+
+        # return the results
+        queue.put([validation_year, model, fold, validation_precision, round(timeit.default_timer() - start_time, 2)])
+
+    except:
+        print(validation_year, model, fold, " failed")
+        pass
+
+def temporal_cv_classification(validation_year, model, queue):
+    start_time = timeit.default_timer()
+    try:
+        # prep validation data
+        validation_df = data[data.SENTENCE_END_YEAR == validation_year]
+        validation_X = validation_df[X_cols]
+        validation_y = validation_df[y_cols]
+
+        # prep training data
+        training_df = data[data.SENTENCE_END_YEAR <= validation_year - 4]
+        training_X = training_df[X_cols]
+        training_y = training_df[y_cols]
+
+        # fit the model
+        clf = clfs[model]
+        clf.fit(training_X, training_y)
+        index_of_class_1 = [i for i,val in enumerate(clf.classes_) if val == 1][0]
+
+        predictions = clf.predict_proba(validation_X)[:, index_of_class_1]
+        validation_precision = precision_at_abs(validation_y, predictions)
+
+        # return the results
+        queue.put([validation_year, model, validation_precision, round(timeit.default_timer() - start_time, 2)])
+
+    except:
+        print(validation_year, model, " failed")
+        pass
+
+def Writer(dest_filename, queue, stop_token, total_len):
+    print(total_len, ' jobs')
+    i = 0
+
+    with open(dest_filename, 'a') as csv_file:
+        writer = csv.writer(csv_file, delimiter=',')
+        while True:
+            line = queue.get()
+            if line == stop_token:
+                return
+            writer.writerow(line)
+            i += 1
+            if i % 10 == 0:
+                csv_file.flush()
+                print(round(i/total_len * 100, 1), " %")
+
+if __name__ == "__main__":
+    data = pd.read_pickle("recid_data.pickle")
+
+    X_cols = data.columns.difference(['RECITIVATED', 'INMATE_DOC_NUMBER', 'SENTENCE_END', 'SENTENCE_START'])
+    y_cols = 'RECITIVATED'
+
+    clfs = {
+        'rf1': RandomForestClassifier(n_estimators=500, n_jobs=1, max_depth=30, max_features='log2', min_samples_split=10),
+        'rf2': RandomForestClassifier(n_estimators=700, n_jobs=1, max_depth=20, max_features='log2', min_samples_split=10),
+        'extra1': ExtraTreesClassifier(n_estimators=200, n_jobs=1, criterion='gini', max_depth=50, max_features='log2'),
+        'extra2': ExtraTreesClassifier(n_estimators=400, n_jobs=1, criterion='gini', max_depth=30, max_features='log2'),
+        'decision1': DecisionTreeClassifier(criterion='gini', max_depth=20, max_features='sqrt', min_samples_split=2),
+        'decision2': DecisionTreeClassifier(criterion='gini', max_depth=50, max_features='sqrt', min_samples_split=2)
+    }
+
+    test_begin_year = 1985
+    test_end_year = 2012
+
+    validation_years = range(test_begin_year-4, test_end_year - 4 + 1)
+    folds = range(0,5)
+
+    # We will begin by pre-generating all the folds so that they can be easily parallelized
+    print("Generating Folds")
+    fold_maker = KFold(n_splits=5, shuffle=True, random_state=4312)
+    indices = {}
+
+    for year in validation_years:
+        print("Validation year: ", year)
+        complete_data = data[data.SENTENCE_END_YEAR <= year]
+
+        indices[year] = {'train':{}, 'test':{}}
+
+        fold_num = 0
+        for train_indices, test_indices in fold_maker.split(complete_data[X_cols]):
+            indices[year]['train'][fold_num] = train_indices
+            indices[year]['test'][fold_num] = test_indices
+
+            fold_num += 1
+
+    print("Folds Generated")
+
+    # Standard ----------------------------------------------------------------
+
+    print('Starting Standard CV')
+    pool = mp.Pool(processes=40)
+    m = mp.Manager()
+    queue = m.Queue()
+
+    STOP_TOKEN="STOP"
+    total_len = len(validation_years) * len(clfs) * len(folds)
+    writer_process = mp.Process(target = Writer, args=("KFold_cv_results.csv", queue, STOP_TOKEN, total_len))
+    writer_process.start()
+
+    pool.starmap(standard_cv_classification, product(validation_years, clfs, folds, [queue], [indices]))
+    queue.put(STOP_TOKEN)
+
+    pool.close()
+    writer_process.join()
+    print('Done with Standard CV')
+
+    # Temporal ----------------------------------------------------------------
+
+    print('Starting Temporal CV')
+    # Because this process is akin to how the model would be used in practice, we
+    # will use it on both the validation years and the final testing years so that
+    # we have modeling precisions for those years as well
+    temporal_years = range(test_begin_year - 4, test_end_year + 1)
+
+    pool = mp.Pool(processes=40)
+    m = mp.Manager()
+    queue = m.Queue()
+
+    STOP_TOKEN="STOP"
+    total_len = len(temporal_years) * len(clfs)
+    writer_process = mp.Process(target = Writer, args=("temporal_cv_results.csv", queue, STOP_TOKEN, total_len))
+    writer_process.start()
+
+    pool.starmap(temporal_cv_classification, product(temporal_years, clfs, [queue]))
+    queue.put(STOP_TOKEN)
+
+    pool.close()
+    writer_process.join()
+    print("cv done, congratulations")
diff --git a/curriculum/3_modeling_and_machine_learning/machine-learning/data_cv/temporal_cv_results.csv b/curriculum/3_modeling_and_machine_learning/machine-learning/data_cv/temporal_cv_results.csv
new file mode 100644
index 00000000..1d552af8
--- /dev/null
+++ b/curriculum/3_modeling_and_machine_learning/machine-learning/data_cv/temporal_cv_results.csv
@@ -0,0 +1,192 @@
+1981,decision2,0.58,2.08
+1982,decision2,0.43,2.49
+1983,decision2,0.35,2.54
+1984,decision2,0.43,2.57
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+1986,decision2,0.4,3.4
+1987,decision2,0.59,3.53
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+1989,decision2,0.63,4.48
+1990,decision2,0.6,4.69
+1991,decision2,0.52,5.08
+1992,decision2,0.53,5.16
+1993,decision2,0.62,5.46
+1981,extra2,0.85,53.64
+1981,rf2,0.82,55.95
+1981,decision1,0.53,0.65
+1981,extra1,0.81,56.6
+1982,extra2,0.83,60.97
+1994,decision2,0.45,4.89
+1982,rf2,0.89,67.07
+1982,decision1,0.53,0.78
+1982,extra1,0.85,68.93
+1995,decision2,0.32,5.38
+1983,extra2,0.84,79.75
+1983,extra1,0.83,79.32
+1983,rf2,0.86,83.31
+1983,decision1,0.54,1.02
+1984,extra2,0.81,88.48
+1984,rf2,0.87,93.24
+1984,decision1,0.62,1.03
+1984,extra1,0.81,92.51
+1996,decision2,0.26,5.81
+1985,extra2,0.83,100.87
+1985,rf2,0.87,107.27
+1985,decision1,0.51,1.27
+1986,extra2,0.9,108.82
+1985,extra1,0.89,106.45
+1997,decision2,0.4,6.39
+1981,rf1,0.89,73.2
+1987,extra2,0.86,130.17
+1987,rf2,0.94,140.62
+1987,decision1,0.65,1.54
+1986,rf2,0.93,143.41
+1986,decision1,0.67,1.51
+1982,rf1,0.87,88.14
+1986,extra1,0.89,146.69
+1998,decision2,0.41,7.08
+1988,extra2,0.91,162.06
+1988,extra1,0.9,165.31
+1988,rf2,0.95,172.02
+1988,decision1,0.69,1.79
+1989,extra1,0.94,171.12
+1999,decision2,0.51,8.6
+1987,extra1,0.82,177.06
+1990,extra2,0.91,183.54
+2000,decision2,0.53,8.29
+1989,rf2,0.94,193.79
+1989,decision1,0.66,2.01
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+1990,rf2,0.95,202.51
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+1984,rf1,0.84,123.17
+2001,decision2,0.41,8.46
+1991,extra2,0.91,228.26
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+1991,decision1,0.55,2.5
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+1992,rf2,0.93,247.27
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+1993,extra1,0.85,270.32
+1986,rf1,0.93,171.63
+1992,extra2,0.89,284.08
+2004,decision2,0.53,11.75
+1993,rf2,0.88,299.22
+1993,decision1,0.71,3.3
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+1995,rf2,0.78,358.0
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+1998,decision1,0.39,4.65
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+1999,rf2,0.89,531.29
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diff --git a/curriculum/3_modeling_and_machine_learning/machine-learning/requirements.txt b/curriculum/3_modeling_and_machine_learning/machine-learning/requirements.txt
index 4b6f499b..2a946b5a 100644
--- a/curriculum/3_modeling_and_machine_learning/machine-learning/requirements.txt
+++ b/curriculum/3_modeling_and_machine_learning/machine-learning/requirements.txt
@@ -4,3 +4,5 @@ numpy
 ipython
 scikit-learn
 statsmodels
+jupyter
+plotnine
diff --git a/curriculum/3_modeling_and_machine_learning/machine-learning/requirements_cv.txt b/curriculum/3_modeling_and_machine_learning/machine-learning/requirements_cv.txt
deleted file mode 100644
index 1c09f708..00000000
--- a/curriculum/3_modeling_and_machine_learning/machine-learning/requirements_cv.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-jupyter==1.0.0
-numpy==1.14.1
-pandas==0.22.0
-plotnine==0.3.0
-scikit-learn==0.19.1