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/optimizer-experiments.md

@@ -45,13 +45,17 @@ GT set = actors X-Ray lists for that scene. Per scene TP/FP/FN, then:
   scenes count more) → **equal-weight mean across movies** (macro; each film counts
   the same regardless of length). This is the DE objective.
 
-Implemented in `scripts/optimizer/scene_score.py`.
+Implemented in `scripts/optimizer/scene_score.py` — since **removed** along
+with this metric; its per-second successor is
+[`scripts/optimizer/second_score.py`](https://REPOLINK/scripts/optimizer/second_score.py)
+(see the [bake-off round](model-bakeoff.md)).
 
 ## The gallery coverage gap
 
 Diagnosing low recall: only **131 of 392** X-Ray cast were in the gallery (33%). Every
 in-gallery actor HAD embeddings (gallery well-formed) — the gap was pure coverage.
-`scripts/optimizer/fetch_missing_actors.py` recovers missing actors:
+[`scripts/optimizer/fetch_missing_actors.py`](https://REPOLINK/scripts/optimizer/fetch_missing_actors.py)
+recovers missing actors:
 `nm-id → TMDB /find external_ids → /person/{id}/images → download → embed (sae_embed)`,
 with a `--wikidata` fallback (P345→P18 Commons photo).
 
@@ -71,7 +75,8 @@ face-recognition pipeline vs X-Ray's presence semantics, not a fixable gap.
 
 ## Optimizer
 
-`scripts/optimizer/optimize.py` — scipy `differential_evolution` over the knob space,
+[`scripts/optimizer/optimize.py`](https://REPOLINK/scripts/optimizer/optimize.py)
+— scipy `differential_evolution` over the knob space,
 each candidate = full replay of all films through the **real** C++ nodes (see the
 KPN replay architecture below) scored by the metric above. Global objective (one
 config for all films, not per-film).
@@ -91,7 +96,8 @@ the tightly-converged knobs were adopted as defaults.
 
 The optimizer never re-decodes video. `scene_analyze --dump-embeddings out.h5` runs the
 expensive half once (decode→detect→align→embed) and dumps per-frame face embeddings
-+ metadata to HDF5 (`scripts/optimizer/SCHEMA.md`). `scripts/optimizer/replay.py` then
++ metadata to HDF5 ([`scripts/optimizer/SCHEMA.md`](https://REPOLINK/scripts/optimizer/SCHEMA.md)).
+[`scripts/optimizer/replay.py`](https://REPOLINK/scripts/optimizer/replay.py) then
 replays that dump through the **real** C++ `face_tracker → identity_matcher →
 scene_tracker` assembled in a Python KPN network (`sae_kpn` nanobind module), varying
 Config knobs freely — no GPU embedding, no decode. Verified BYTE-EXACT against
@@ -110,8 +116,10 @@ python scripts/optimizer/optimize.py --manifest films.json --gallery gallery.jso
     --params prob_threshold:0.5:0.999 anneal_sec:1:30 extinction_sec:1:15 \
     --popsize 8 --maxiter 20 --trajectory traj.jsonl --out opt.json
 # 4. score a fixed config / validate on a held-out set
-python scripts/optimizer/score_config.py --manifest heldout.json --gallery gallery.json \
-    --config '{"prob_threshold":0.76,"extinction_sec":1.5,"anneal_sec":10}'
+#    (historical: score_config.py and scene_score.py were removed with the
+#     scene-union metric — use scripts/optimizer/second_score.py, per-second)
+python scripts/optimizer/second_score.py --help
 ```
 
-See also memory: kpn-python-replay-optimizer, gallery-coverage-gap, xray-validation-*.
+Superseded by the [model bake-off + re-tune](model-bakeoff.md), which
+replaced this round's scene-union metric with per-second scoring.