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

apps/server/.env.example

@@ -99,3 +99,8 @@ WS_ALLOWED_ORIGINS=http://localhost:5173,tauri://localhost,http://tauri.localhos
 # CARWASH_REVIEW_TOKEN=
 # CARWASH_REVIEW_BOOTH_ID=
 # CARWASH_REVIEW_INTERVAL_SEC=60
+# Entry-stream sampling: also queue one in N ENTRY vehicle reads (no wash, no operator) as
+# pure training material in the gate view — many times the wash stream, zero domain shift.
+# 1 = every entry (the reviewer labels what they have time for; the rest waits and stays
+# useful), N = one in N, 0/unset = off. Needs the three settings above.
+# CARWASH_REVIEW_ENTRY_SAMPLE=1