Re-aligned heuristic, updated readme, added code comment

README.md

@@ -30,14 +30,18 @@ Average silhouette width is a widely-used measure of clustering quality. Higher
 
 2. How well-separated each cluster is from its nearest neighboring cluster.
 
+Although the coefficient is mathematically bounded by [−1, 1], cosine distance between high-dimensional text embeddings is compressed: unrelated notes sit close to orthogonal, so both the within-cluster and nearest-cluster distances land near 0.8. Because silhouette divides the gap between them by the larger of the two, the practical range on embedding data is roughly [−0.05, 0.10] rather than the full interval.
+
+The bands below are therefore calibrated against that observed range. On the seed board, the five ideal thematic clusters score 0.09; swapping a few notes between clusters drops it to 0.06; a scrambled assignment falls below zero.
+
 The score appears above the results with a plain-language band:
 
-- **0.70 and above** — Strong
+- **0.07 and above** — Strong
-- **0.40 to 0.69** — Moderate
+- **0.04 to 0.06** — Moderate
-- **0.10 to 0.39** — Weak
+- **0.01 to 0.03** — Weak
-- **Below 0.10** — Poor
+- **Below 0.01** — Poor
 
-Silhouette values are archetypically bounded below 1.0 for real-world data, so the number is best read as a relative measure. See Hugo Sträng, Tai Dinh. An upper bound on the silhouette evaluation metric for clustering. Pattern Recognition, Volume 178, 2026, 113402, ISSN 0031-3203.
+A score near 0.00 means the grouping is no better than chance. Bands are specific to `voyage-3` cosine distance and would need recalibration behind a different embedding model. See Hugo Sträng, Tai Dinh. An upper bound on the silhouette evaluation metric for clustering. Pattern Recognition, Volume 178, 2026, 113402, ISSN 0031-3203.
 
 ## Organizing clusters, exporting to workflow software
 

frontend/src/components/stickies.tsx

@@ -6,10 +6,14 @@ import Sticky from './sticky';
 import Button from './button';
 import '../styles/stickies.css';
 
+// In practice, silhouette on cosine distance between text embeddings occupies roughly
+// [-0.05, 0.10], not strict theoretical [-1, 1]: near-orthogonal vectors put both the within- and
+// nearest-cluster distances close to 0.8, and the coefficient divides their gap
+// by the larger. These bands are calibrated to that range for voyage-3. see README, Reading the cohesion score
 const scoreLabel = (score: number): string => {
-  if (score >= 0.7) return 'Strong';
+    if (score >= 0.07) return 'Strong';
-  if (score >= 0.4) return 'Moderate';
+    if (score >= 0.04) return 'Moderate';
-  if (score >= 0.1) return 'Weak';
+    if (score >= 0.01) return 'Weak';
     return 'Poor';
 };
 

frontend/src/tests/stickies.test.tsx

@@ -14,7 +14,7 @@ const MOCK_CLUSTER_RESPONSE = {
     { label: 'Auth Issues', noteIds: ['note_001'] },
     { label: 'Export Issues', noteIds: ['note_002'] },
   ],
-  score: 0.74,
+  score: 0.09,
 };
 
 let fetchMock: ReturnType<typeof vi.fn>;