diff --git a/src/municipalities/02-CA.R b/src/municipalities/02-CA.R
index 5134a8c..cf0c282 100644
--- a/src/municipalities/02-CA.R
+++ b/src/municipalities/02-CA.R
@@ -78,7 +78,7 @@ col_sup_politics <- analysis_vars[
 ]
 
 # (c) Infrastructure, mobility, and event counts → col.sup
-# Genuine counts but measuring dwellings, vehicles, farms, animals, or flows
+# Counts but measuring dwellings, vehicles, farms, animals, or flows
 # rather than resident persons. Projecting them as supplementary shows how
 # they relate to the population-composition space without distorting it.
 col_sup_infra <- analysis_vars[
@@ -116,10 +116,7 @@ col_sup_vars <- c(col_sup_edu, col_sup_politics, col_sup_infra)
 
 # Active: population-composition person-counts
 # All five groups answer "how many residents have attribute X?" and therefore
-# share a common denominator — the resident population. No pre-processing
-# or proportionalization is needed: CA row-profile normalisation already
-# removes the size effect, and chi-square distance directly compares
-# compositional profiles.
+# share a common denominator.
 active_vars <- analysis_vars[
   (str_detect(analysis_vars, "^age_") |
     str_detect(analysis_vars, "^education_level_of_swedish_men_") |