feat(carwash): entry-stream sampling for the review outbox; park-2 wired to the collector

The wash stream is small; the entry camera photographs every car in exactly the view the
classifier is trained on. The booth can now queue entry vehicle reads as pure training
material — crop + the camera's class, no order, no operator, no category.

- Core announces every vehicle read (deviceEvents.emitVehicleRead from snapshot.ts); the
  Car Wash module listens, samples entry reads in-process (sampleEntry: exactly one in N)
  and queues them (enqueueEntry). CARWASH_REVIEW_ENTRY_SAMPLE=N; 1 = every entry (storage
  and bandwidth are not the limit — user); 0/unset = off. Forwarded by compose.
- Packages carry kind: "wash" | "entry". Collector: kind column, entry meta validated
  without the operator fields, review screen shows an entry sample as such, export has a
  kind column, operator agreement computed from wash items only. Setup line shows
  "1 in N entries sampled"; status carries entrySample.
- komodo: park-2's four review lines enabled (collector URL by Netbird DNS name, booth-2,
  the shared per-booth secret, every entry sampled) — the collector is up on the overlay.
- Tests on both sides. Wiki: vision-review-outbox (entry stream + the internet-feed
  assessment), log.

Claude-Session: https://claude.ai/code/session_01FWncR69HgGPuei1dLrW3cU

wiki/concepts/vision-review-outbox.md

@@ -39,6 +39,24 @@ locked-down collector without exposing anything to the open internet.
    image is dropped from the row once delivered; a voided order is abandoned unsent; anything
    older than 14 days is abandoned ("expired") rather than resurfacing a fortnight in a burst.
 
+## The entry stream — the real accelerator (built 2026-09-07)
+
+The wash stream is small; the **entry camera photographs every car**, in exactly the view the
+classifier is trained on, with zero domain shift. So the booth can also queue **one in N entry
+vehicle reads** as pure training material: the crop and the camera's class, *no* order, *no*
+operator, *no* category — same crop-and-blur pipeline, same one-way path, same privacy
+properties. `CARWASH_REVIEW_ENTRY_SAMPLE=N` (0/unset = off; needs the three upload settings).
+Seam: the core announces every vehicle read (`deviceEvents.emitVehicleRead`, snapshot.ts, entry
+and exit) and the Car Wash module decides — it samples entry reads in-process (`sampleEntry()`,
+exactly one in N) and calls `enqueueEntry()`; the core never imports the module. Packages carry
+`kind: "wash" | "entry"`; the collector stores the kind, the review screen shows an entry sample
+as "entry stream — label the vehicle", the export carries a `kind` column, and **operator
+agreement is computed from wash items only** (an entry sample has no operator decision).
+
+An internet feed was considered the same day and kept OUT of the collector's ingest: licensed
+sets only, in a separate folder with provenance, used as warm-up and weighted down, and never
+the judge of accuracy — the evaluation set is gate crops only.
+
 ## The package
 
 `multipart/form-data`: `meta` (JSON) + `image` (JPEG). Meta = `{ v, booth, item, order, at,