diff --git a/examples/SurfaceFluxExample/kappa_calibration.jl b/examples/SurfaceFluxExample/kappa_calibration.jl
index 388a411d9..fef12076f 100644
--- a/examples/SurfaceFluxExample/kappa_calibration.jl
+++ b/examples/SurfaceFluxExample/kappa_calibration.jl
@@ -253,7 +253,7 @@ ylabel!("U^*")
 png("our_plot")
 # ![see plot: ](../assets/kappa_calibration_plot1.png)
 
-# We also plot the unconstrained κ values across all ensembles before and after the EKI process in a histogram.
+# We also plot the constrained κ values across all ensembles before and after the EKI process in a histogram.
 histogram(initial_ensemble[1, :], label = "initial")
 histogram!(final_ensemble[1, :], label = "final")
 xlabel!("κ")
@@ -261,8 +261,5 @@ ylabel!("# of Ensembles")
 png("final_and_initial_ensemble")
 # ![see plot: ](../assets/kappa_calibration_plot2.png)
 
-#=
-- discuss pipeline
-- perfect model experiment ok
-- functional learning extension?
-=#
+# Evidently, EKI was highly successful at recovering the von karman constant κ = 0.4. This process will be extended to recover
+# stability function parameters such as a_m, a_h, b_m, b_h, and Pr_0.