docs: opencv5 experiment log + rewritten Home

Add model-bakeoff.md for the opencv5 build: the ten-knob DE tuning and where
each shipped config default comes from, the replay architecture, and the
flood-fill-on-learned-boundaries step change (62.6% -> 74.9% presence F1, LOO).
Rewrite index.md to lead with the learned scene-boundary result and point at
the current pages, with the July four-model bake-off moved to an Archive
section. Both pages build with no broken links.

docs/index.md

@@ -17,62 +17,70 @@ two credited cast members without a visible face are correctly reported
 present but not visible. This matches Amazon X-Ray's own record for this
 second exactly.
 
-Results are not uniform across films. The hardest held-out film scores 46%
-F1. This report documents why: one tunable trade (extinction bridging at
-hard cuts), one structural limit (X-Ray credits people whose faces never
-appear on screen), and a small number of cases where the pipeline is
-correct and X-Ray's ground truth is not. Read
-[how we score against X-Ray](methodology.md) first. X-Ray's ground truth is
-scene-level; the pipeline's output is per-second. That difference shapes
-every finding below.
+## The headline: learned scene boundaries
 
-## Findings
+The current opencv5 build's biggest gain is **flood-fill presence on a
+learned scene-boundary detector**. An actor seen once inside a shot is
+reported for the whole shot — but only if the shot boundaries are good. A
+learned XGBoost boundary detector, scored **leave-one-out** so no film is
+ever measured by a detector that trained on it, lifts per-second X-Ray
+presence F1 across nine films and improves every one of them:
 
-<div class="grid cards" markdown>
+| boundary source for flood-fill | presence F1 |
+| ------------------------------ | ----------: |
+| track-extent (flood off) | 62.6% |
+| flood + grayscale cuts | 64.0% |
+| **flood + learned detector (LOO)** | **74.9%** |
 
--   :material-trophy:{ .lg .middle } **[Which model is best?](best-model.md)**
+![Macro presence F1 by flood-fill boundary source](assets/images/scene_presence_macro.png)
 
-    ---
+The full story — why the old grayscale cut detector broke Scarface, what
+features work, and the per-film breakdown — is on the
+[learned scene-boundary detector](scene-boundary-detector.md) page.
 
-    Calibration curves first, independent of any threshold, then held-out
-    F1 across three models. LVFace-B Glint360K wins both, and wins on every
-    held-out film.
+## What the numbers mean, and their limits
 
--   :material-filter:{ .lg .middle } **[Whole vs. cast-restricted gallery](gallery-scope.md)**
+Results are not uniform across films, and they should not be. X-Ray's ground
+truth is scene-level and credits people whose faces never appear on screen;
+the pipeline's output is per-second and can only name a face it can see.
+That difference is a structural recall ceiling, not a bug. Read
+[how we score against X-Ray](methodology.md) first — it defines F1,
+precision, recall, and misID, and explains the two limits (off-screen cast
+and gallery coverage) that shape every finding.
 
-    ---
-
-    Restricting the matcher to a film's credited cast improves F1,
-    recall, and misID rate at once, but is not a shipped runtime feature
-    yet.
-
--   :material-account-convert:{ .lg .middle } **[Does pose expansion help?](pose-expansion.md)**
-
-    ---
-
-    A training-set effect that did not reproduce on 5 held-out films once
-    two methodology bugs in the comparison harness were found and fixed.
-
--   :material-magnify-expand:{ .lg .middle } **[Deep dive: LVFace-B Glint360K](lvface-deep-dive.md)**
-
-    ---
-
-    The held-out generalization gap, the two mechanisms behind its errors,
-    and every distinct case where it names someone outside the film's
-    credited cast.
-
-</div>
+Precision on identified faces is near-perfect: where the pipeline names a
+face, it is almost always a name X-Ray also credits to that scene. The
+frames throughout this documentation make the tension visual — **green** =
+true positive, **red** = false positive, **orange** = unknown, and a
+**blue** panel lists credited cast present with no visible face.
 
 ## Full experiment log
 
-- **[Full experiment log](model-bakeoff.md)**: the complete log behind the
-  four pages above, including how replaying against cached embeddings
-  inside the same KPN network makes a full model and configuration
-  comparison practical, the full results table, and every caveat. This is
-  where the shipped [`src/config.hpp`](https://REPOLINK/src/config.hpp)
-  defaults come from.
-- **[Service conversion (proposal)](service-conversion.md)**: design
-  sketch for a native idle-GPU worker gated on screen lock, not yet built.
+- **[Full experiment log (opencv5)](model-bakeoff.md)**: the complete log
+  behind the current build — the ten-knob differential-evolution tuning, the
+  shipped [`src/config.hpp`](https://REPOLINK/src/config.hpp) defaults and
+  where each comes from, the replay architecture that makes a nine-film
+  search tractable, and the flood-fill step change.
+- **[Learned scene-boundary detector](scene-boundary-detector.md)**: the
+  features, the model, leave-one-out results, and the two headline films.
+- **[Benchmark — SuperHero](benchmark.md)**: the benchmark harness.
+- **[Service conversion (proposal)](service-conversion.md)**: design sketch
+  for a native idle-GPU worker gated on screen lock, not yet built.
+
+## Archive (July 2026)
+
+The pre-opencv5 four-model ArcFace/LVFace bake-off is kept for provenance.
+Its numbers are historical; the current build supersedes them.
+
+- [Best model (July)](best-model-2026-07.md) — LVFace-B Glint360K wins on
+  calibration and on every held-out film.
+- [Gallery scope (July)](gallery-scope-2026-07.md) — cast-restricted
+  gallery improves F1, recall, and misID at once.
+- [Pose expansion (July)](pose-expansion-2026-07.md) — a training-set
+  effect that did not reproduce held-out.
+- [LVFace deep dive (July)](lvface-deep-dive-2026-07.md) — the
+  generalization gap and every out-of-cast identification.
+- [Full experiment log (July)](model-bakeoff-2026-07.md).
 
 ## Reproducing the benchmarks