docs: richer report — data figures, success/failure frames, commit-pinned repo links

- experiment_charts.py generates 4 figures from experiments/ artifacts:
  held-out per-film F1, 16-combo ranking, DE search landscape, and the
  Downton detector-vs-tracker ghost timeline (replaces the blank
  title-card screenshot)
- new frames: 19-correct wedding shot (success case), Many Saints
  ghost-vs-unknown frame (three error classes in one image)
- rename rep4-optimizer-results.md -> model-bakeoff.md; rep4 kept only
  as the on-disk artifact prefix, explained once
- repo file references are now links via https://REPOLINK/<path>
  placeholders; build_site.sh pins them to the HEAD commit's raw URLs
  and fails the build if a linked path doesn't exist at HEAD
- drop references to removed scripts (scene_score.py, score_config.py)
  and to session-memory names; mark artifact-registry paths with their
  pull commands
- commit readme_example.jpg + pipeline_topology.svg so README renders
  on the plain Gitea repo view
- deploy_pages.sh: push built site/ to the gitea-pages branch

docs/best-model.md

@@ -9,7 +9,7 @@ open question: is LVFace (455MB) actually better, or just the biggest?
 
 Each gallery carries a fitted Platt sigmoid `P(match | cosine similarity) =
 σ(a·sim + b)`, embedded directly in the gallery's HDF5 file
-(`src/gallery/gallery_calibration.hpp`). This is a property of the embedding
+([`src/gallery/gallery_calibration.hpp`](https://REPOLINK/src/gallery/gallery_calibration.hpp)). This is a property of the embedding
 space alone — computed from intra/inter-actor reference-image pairs, no
 tracking or scene logic involved — so it's a clean first read on discriminative
 power before running a single benchmark.
@@ -31,7 +31,7 @@ head start before the tracking/scoring pipeline is even involved.
 ## Second signal: F1 on the actual benchmark
 
 Best full-gallery (no cast-restriction) result per model, from the 16-combo
-rep4 matrix (`rep4-optimizer-results.md`):
+bake-off matrix ([full experiment log](model-bakeoff.md)):
 
 | model | F1 | P | R | misID |
 |---|---|---|---|---|
@@ -40,16 +40,24 @@ rep4 matrix (`rep4-optimizer-results.md`):
 | ArcFace R18 | 69.1% | 87.6% | 57.7% | 242 |
 | ArcFace w600k-R50 | 68.5% | 94.0% | 54.1% | 150 |
 
+The full 16-combo picture makes the model ordering visible at a glance — LVFace
+(yellow) tops both the restricted and full columns, and R18 (green) props up
+the bottom of the full-gallery ranking:
+
+![All 16 bake-off combos ranked by training-set F1](assets/images/rep4_matrix_f1.png)
+
 LVFace wins outright, with the highest recall of any full-mode combo. This
 reverses an earlier conclusion from a prior (superseded) benchmarking pass
 using a scene-union metric, which found the three models statistically
 indistinguishable (~85% each) and concluded LVFace wasn't worth its size — that
 metric hid out-of-cast false positives behind a gallery∩cast recall mask (see
-`optimizer-experiments.md`); the per-second metric used here does not.
+[the prior optimizer round](optimizer-experiments.md)); the per-second metric
+used here does not.
 
 Held-out validation (5 films never seen by the optimizer) confirms LVFace's
-lead holds up out of sample — see the deep-dive page for the full breakdown,
-including where it fails.
+lead holds up out of sample — see the
+[LVFace deep dive](lvface-deep-dive.md) for the full breakdown, including
+where it fails.
 
 ## Caveat: model choice is an operational change
 
@@ -57,5 +65,6 @@ Switching the default embedder isn't just flipping a config value — the
 gallery itself is model-specific (embeddings from different models aren't
 comparable), so any existing gallery built against ArcFace w600k-R50 needs to
 be rebuilt from source images against LVFace before the new default takes
-effect. `scripts/optimizer/reembed_gallery.py` does this from a reference
-gallery's cached source images without re-downloading anything.
+effect. [`scripts/optimizer/reembed_gallery.py`](https://REPOLINK/scripts/optimizer/reembed_gallery.py)
+does this from a reference gallery's cached source images without
+re-downloading anything.