docs: rep4 bake-off write-up, MkDocs site, artifact-registry-backed experiments

docs/rep4-optimizer-results.md is the main deliverable: the model bake-off +
threshold re-tune experiment log, including the ROCm teardown deadlock root
cause and fix, DE concurrency tuning, the 16-combo results table, held-out
validation against 5 films never seen by the optimizer (macro F1 67.4% vs.
75.3% training — a real generalization gap), the frozen-bbox "ghost track"
failure mode found via annotated frame evidence, calibration curves per model,
and an isolated-effects breakdown of gallery scope vs. pose expansion.

MkDocs site (mkdocs.yml, docs/index.md) renders docs/*.md; scripts/docs/
pulls referenced images from the artifact registry and generates the
calibration chart at build time (see the tooling commit) rather than
committing images to the repo.

experiments/ now keeps only scripts + README + SESSION_STATE.md in git — every
data artifact (galleries, dumps, X-Ray corpus, montage frames, trajectories,
manifests, results) moved to the Gitea package registry. film-lut.template.json
is the committed placeholder for the gitignored file-lut.json (real local
movie paths, never shared — some source filenames carry scene-release tags).

Adds models/transnetv2.onnx (via Git LFS, matching the other ONNX models) for
the new scene-detection path.

experiments/README.md

@@ -0,0 +1,59 @@
+# experiments/ — X-Ray validation & optimizer artifacts
+
+Durable home (in the repo tree, NOT `/tmp` scratch — a scratch wipe once cost an hour)
+for the data behind the X-Ray threshold-optimization and embedding-model bake-off.
+
+## Layout
+- `xray/` — Amazon X-Ray Zenodo dataset (gitignored, ~140MB; DOI 10.5281/zenodo.17659734).
+- `dumps/` — per-model embedding dumps, one HDF5 per (model, film). Gitignored (large).
+  Naming: `<model>/dump_<Film>.h5`. Regenerate with `scene_analyze --dump-embeddings`.
+- `galleries/` — per-model galleries (gitignored JSON). `gallery_<model>.json` +
+  augmented variants. Regenerate with build_gallery / fetch_missing_actors.
+- `manifests/` — film manifests (committed — small, and the Jellyfin ID join is the
+  authoritative record of which films/paths/X-Ray-dirs were used).
+- `trajectories/` — DE trajectories, one JSONL per run (committed — the evidence).
+- `results/` — final per-run metrics + the model comparison table (committed).
+
+## Embedding-model bake-off (July 2026)
+
+Question: is LVFace-B (455MB) actually the best vs X-Ray, or just the biggest?
+Method: **optimize per model** — each model gets its own dumps + gallery + full DE run,
+then compare each model at ITS OWN optimum (fairest — no model penalised by another's
+threshold). Scored by the weighted per-scene metric (out-of-cast misID ×10; see
+docs/optimizer-experiments.md).
+
+Models:
+| model | file | size | MovieNet rank-1 (prior) |
+| ----- | ---- | ---- | ----------------------- |
+| LVFace-B_Glint360K | models/LVFace-B_Glint360K.onnx | 455 MB | — |
+| ArcFace w600k R50  | models/arcface_w600k_r50.onnx  | 174 MB | 85.2% |
+| ArcFace R18        | models/arcface_r18.onnx        | 48 MB  | 72.2% |
+| ArcFace w600k MBF  | models/arcface_w600k_mbf.onnx  | 13 MB  | 83.3% |
+
+9 genuine X-Ray-overlap films (Jellyfin ID join): Benny & Joon, Café Society,
+Downton Abbey: A New Era, Lord of War, Lovelace, The Many Saints of Newark, Scarface,
+Sound of Metal, Valerian.
+
+## Gallery-mode bake-off (full vs cast-restricted)
+
+Second axis alongside the model comparison: does restricting the matcher's candidate
+set to a title's credited cast reduce cross-film misIDs (e.g. naming Archie Yates in a
+film he's not in) vs. matching against the whole 2418-actor gallery?
+
+- **full** — match against the entire model gallery (2418 actors).
+- **restricted** — per film, match only against its Jellyfin credited cast, filtered
+  from the gallery by jellyfin_id. This is what run_from_jellyfin.py does in production.
+
+**LIMITATION — Jellyfin stores only ~15 actors per title.** Jellyfin's People list is
+capped at the top-billed cast (~15 Actors), NOT the full IMDb/X-Ray cast (e.g. Scarface:
+Jellyfin 15 vs X-Ray 67). This is a hard limit of the metadata Jellyfin imports — not a
+query parameter (verified: /Items?Fields=People returns 15 regardless; the single-item
+/Items/{id} endpoint 400s on this server). So the "restricted" arm restricts to the ~15
+top-billed leads, which caps its achievable recall at whatever fraction of on-screen
+actors are top-billed, but should drive out-of-cast misIDs toward zero. A production
+deployment wanting fuller cast restriction would need a richer cast source than Jellyfin
+(TMDB/IMDb full credits).
+
+Matrix: 4 models × {full, restricted} = 8 DE runs, all reusing the 36 dumps + 4 baseline
+galleries (no augmentation — avoids test-set leakage on either arm). Scored by the
+duration-weighted per-scene metric with the misID split (report_rates.py).