Re-aligned heuristic, updated readme, added code comment

Reviewed-on: #2

README.md

@@ -14,7 +14,7 @@ Two models run in parallel, and neither sees the other's work. Anthropic's `clau
 
 At the same time, Voyage AI's voyage-3 model (`backend/services/embedding.service.js`) converts each note's text into a numeric representation of its semantic meaning aka vector. 
 
-Once the LLM returns, kongruity scores that grouping (`backend/services/validation.service.js`) using an established silhouette coefficient, with cosine distance rather than Euclidean as the distance metric. 
+Once the LLM returns, kongruity scores that grouping (`backend/services/validation.service.js`) using an established silhouette coefficient, with cosine distance rather than Euclidean as the proximity metric. 
 
 For each note, it weighs the average distance to the other notes in its own cluster against the average distance to the notes in the nearest neighboring cluster. Averaged across every note, this yields a single cohesion score in the range [−1, 1], displayed at the top of the results.
 
@@ -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
-- **0.40 to 0.69** — Moderate
-- **0.10 to 0.39** — Weak
-- **Below 0.10** — Poor
+- **0.07 and above** — Strong
+- **0.04 to 0.06** — Moderate
+- **0.01 to 0.03** — Weak
+- **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 >= 0.4) return 'Moderate';
-  if (score >= 0.1) return 'Weak';
+    if (score >= 0.07) return 'Strong';
+    if (score >= 0.04) return 'Moderate';
+    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>;