diff --git a/README.md b/README.md
index edb34a9..56e3dec 100644
--- a/README.md
+++ b/README.md
@@ -6,12 +6,14 @@ database connection is needed.
 
 ## How to run
 ### Activate a python env and install deps
-`python -m venv .venv`
+`python -m venv .venv` (I used python version 3.13.3)
 `source ./.venv/Scripts/activate`
 `pip install -r requirements.txt`
 
 ### Run all experiments
 `python run_all_experiments.py`
+or when wanting to re run everything from scratch
+`python run_all_experiments.py --start-fresh`
 
 (The Weka JRip baseline additionally needs a Java runtime; it is skipped
 automatically if `java`/`weka.jar` are unavailable.)
@@ -19,3 +21,6 @@ automatically if `java`/`weka.jar` are unavailable.)
 ### Further info
 - `./results` holds all saved data behind the figures, including the learned rules.
 - the results that were used are in the subfolder `/pooled` (pooled over the 5 folds).
+- `supplementary_diagnostics.py` reproduces the secondary analyses referenced in the
+  Discussion (extreme-lambda behaviour and the combined grow+prune+MDL variant). These are
+  exploratory, single-repeat checks and are not part of the primary study.
diff --git a/diagnostics_extreme_and_combined.py b/supplementary_diagnostics.py
similarity index 91%
rename from diagnostics_extreme_and_combined.py
rename to supplementary_diagnostics.py
index b07e4cc..047c195 100644
--- a/diagnostics_extreme_and_combined.py
+++ b/supplementary_diagnostics.py
@@ -1,5 +1,12 @@
-"""Scratch: (1) extreme-lambda effect on onto-MDL vs onto-grow, (2) combined grow+prune+MDL
-variant vs all others. ONE repeat (5 folds) pooled per point — quick look, not a full sweep."""
+"""Supplementary diagnostics referenced in the Discussion (not part of the primary study).
+
+Two secondary analyses, each pooled over one repeat of 5 folds (runtime measurement off):
+  1. Effect of extreme lambda values on onto-MDL vs onto-grow guidance.
+  2. The combined grow+prune+MDL variant compared against all other guidance variants.
+
+These use a single repeat per point as an exploratory check rather than the full repeated
+sweep behind the main results, so the numbers are indicative rather than definitive.
+"""
 import numpy as np, pandas as pd
 from collections import defaultdict
 from sklearn.model_selection import RepeatedStratifiedKFold