docs: calibration matrix — ONNX vs GGUF vs highheelbgz × ICC profiles

5 encoding paths × ONNX ground truth × 6 models × 6 roles:
  ONNX (rten) = f32 ground truth
  GGUF raw u8 CDF, GGUF γ+φ, GGUF i8 signed, GGUF highheelbgz spiral

ICC profile per path. Spearman ρ identifies best encoding per model×role.
All tools ready: rten, streaming, gamma_phi, LensProfile, calibrate harness.
Estimated: ~2.5 hours for complete matrix.

https://claude.ai/code/session_01ChLvBfpJS8dQhHxRD4pYNp

Author: claude

.claude/HANDOVER_MAVERICK_SESSION.md

@@ -581,3 +581,68 @@ Calibration pipeline:
 Both ONNX and GGUF from SAME model = no API key, no network for calibration.
 Jina v5 replaces v3 as truth anchor (newer, better, has ONNX).
 ```
+
+---
+
+## CALIBRATION MATRIX — The Definitive Experiment
+
+### Three encoding paths × ONNX ground truth
+
+```
+For each model (Jina v5, BGE-M3, Reranker, Reader-LM, Qwopus, Maverick):
+  For each role (Q, K, V, Gate, Up, Down — or token_embd for embedding models):
+  
+    1. ONNX (rten): load model.onnx → forward pass → f32 embeddings = GROUND TRUTH
+    2. GGUF raw:    stream BF16 → CLAM → cosine → u8 HDR CDF table
+    3. GGUF γ+φ:    stream BF16 → CLAM → cosine → gamma offset → phi redistribute
+    4. GGUF i8:     stream BF16 → CLAM → cosine → signed i8 (preserves inhibition)
+    5. GGUF hhbgz:  stream BF16 → CLAM → highheelbgz spiral → golden ratio stride
+    
+    ICC profile: compare each path (2-5) against ground truth (1)
+    Measure: Spearman ρ, transfer curve, noise floor, effective bits
+    
+    Best path = highest ρ after ICC correction
+    Per-model per-role winner may differ!
+```
+
+### Why this is definitive
+
+```
+Current state: we ASSUME our encoding preserves topology.
+After calibration: we KNOW, quantified to 4 decimal places.
+
+Expected outcomes:
+  - i8 wins for reranker (symmetric cos range, sign matters)
+  - γ+φ wins for gate-heavy roles (concentrates resolution at zero)
+  - raw u8 CDF is surprisingly good for embedding models (positive-skewed)
+  - hhbgz spiral wins for... we don't know yet. That's why we test.
+  
+  ICC correction makes EVERY path usable.
+  But some paths need less correction = more faithful = preferred.
+```
+
+### Tools ready
+
+```
+rten:                 AdaWorldAPI/rten (ONNX runtime, your fork)
+GGUF streaming:       stream_hdr_lens.rs, stream_maverick.rs (HTTP range)
+highheelbgz:          spiral addressing + golden ratio stride
+bgz-tensor:           gamma_phi.rs (GammaProfile, encode/decode)
+LensProfile:          lance-graph-contract/high_heel.rs (ICC DTO)
+LensConfig:           lance-graph-contract/high_heel.rs (6-model registry)
+calibrate_lenses.rs:  Spearman ρ + ICC builder harness
+Jina v5 ONNX:         jinaai/jina-embeddings-v5-text-small-text-matching
+Jina v5 GGUF:         same repo, F16.gguf (1.2 GB, streamable)
+```
+
+### Session estimate
+```
+Download: Jina v5 ONNX (2.4 GB) + GGUF (1.2 GB) = 3.6 GB (fits in 18 GB free)
+Compute:  rten inference on 1000 texts ≈ 5 min
+          CLAM + 5 encoding paths ≈ 10 min
+          ICC profiles ≈ 1 min
+          Total: ~20 min for Jina v5 complete calibration
+          
+Repeat for remaining 5 models: ~2 hours total
+Full calibration matrix: ~2.5 hours
+```