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          "rows": 2320,
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          "won_output_rows": 2320
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  "archive_root": "union-staging",
  "book_audit": {
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      {
        "file": "2026-10-28T06.book_dropped.parquet",
        "rows": 12973,
        "sha256": "af5744d030e07db892cb5ef06be58d96f973f4fb17061ba70f71df3be289d159",
        "size": 511812
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    "parts": 1,
    "public": false,
    "rows": 12973,
    "status": "kept until verify",
    "verified": false,
    "verify_check_id": null
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  "book_rows_dropped": 12973,
  "boundary_equal": true,
  "boundary_guard": "cache",
  "canonical_token_map": {
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    "sha256": "54687d296e19a0bab637bb20fc3d4d5ea6b5c474c0db29ac86b01839fbf54c82"
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      "created_at": "2026-10-28T19:45:18Z",
      "map_health_sha256": "742417dd2a77d6c8c94df0c1bcf16c026cee03d5d9c0519ad7f64bb3cdf8199d",
      "map_rule": "native-category-ranked-subtag-v1",
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    "evaluated_at": "2026-10-28T23:25:11Z",
    "evidence": {
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      "sha256": "735d51b654bd84769b90042086b9fda7fcb00037ab818807549f40c4d0809ad0"
    },
    "hour": "2026-10-28/06",
    "low_tag": {
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      "band_rule": "floor",
      "flagged": false,
      "floor": 0.9,
      "mad": null,
      "median": null,
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      "changed_markets": null,
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      "file": "category_map.json.gz.health.json",
      "logic_identity_changed": null,
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      "query_ok": true,
      "review_acceptance": {
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        "map_sha256": "dfd8d842e504d36618bb15b18ca2d1ec776dfbab4c0c09f136cf92b7ad796cab",
        "note": "gate v6 (live2 relaunch, operator GO on CYCLE-10616, 2026-10-28), reviewed by Capture against gate v5b map 831fd988: 5,135,317 markets, 52,471 added, 0 gone (no shrink), 205,948 labels changed (4.052%), mostly uncategorised -> a venue category (crypto 83,451, sports 68,910, esports 35,052, weather 8,939); rule native-category-ranked-subtag-v1, tag list 1c81c469"
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        },
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        "lt_24h": {
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        "file": "category_map.json.gz.health.json",
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          "note": "gate v6 (live2 relaunch, operator GO on CYCLE-10616, 2026-10-28), reviewed by Capture against gate v5b map 831fd988: 5,135,317 markets, 52,471 added, 0 gone (no shrink), 205,948 labels changed (4.052%), mostly uncategorised -> a venue category (crypto 83,451, sports 68,910, esports 35,052, weather 8,939); rule native-category-ranked-subtag-v1, tag list 1c81c469"
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        "problem": "no band given: the fixed floor alone",
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  "mirror_event_types": [
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  "mirror_exceptions_by_field": {
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  "mirror_exceptions_kept": {
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  "mirror_rows_dropped": {
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  "mirror_rule": "mirror-exact-v2",
  "notes": "A lean-file reader who ignores `mirror_dropped` misses the complement token's updates. Every stored row's timestamp_received is its lean timestamp + `recv_lag_ms` (whole milliseconds); the provenance does not store it. A dropped twin rebuilds exactly from the canonical row plus the sidecar's twin columns: sequence = the canonical row's sequence + `twin_seq_delta`, timestamp_received = the canonical row's + `twin_recv_delta_ns`, arrival_skew = `twin_arrival_skew` (provenance sidecar), recv_lag_ms = the canonical row's + `twin_recv_delta_ns` / 1,000,000, so the expansion, sorted per event type by (timestamp, sequence, row_ordinal), orders the complement stream exactly as in v3: a rebuilt twin sorts by its own sequence, not beside its canonical row, and never ties (a twin is dropped only when no other row of its event type has its market, token, timestamp and sequence), and tied stored rows keep their source order in row_ordinal. Repeat books (defined below) are dropped under book-exact-repeat-v1 and not rebuilt, so replay of a v3.1 hour is book-equivalent to v3, not message-identical. A repeat book is kept, not dropped, on a token with no price_change in the hour's expanded stream (the stored rows plus the rebuilt twins, so a complement token's dropped price_change twins count), or within 2,000 ms (inclusive, before or after, by venue `timestamp`) of one of that token's price_change or tick_size_change rows in that stream, so a file can still hold consecutive identical books. Every v3 column of every stored or rebuilt row comes back exactly (dropped repeat books are not rebuilt), with timestamp_received and arrival_skew floored to the millisecond: `spread` is best_ask - best_bid on best_bid_ask rows and null on every other row, except on rows whose provenance `spread_differs` is true, where it is `spread_raw` (a twin takes its canonical row's), and `source_witness` is in the provenance. The hour's twin_check file counts and fingerprints (twin-check-v1), per category, market and event type, the dropped twins, the stored rows and all rows of the expansion, so a reader can check both what it reads and what it rebuilds. min_sequence and max_sequence (top level and per product) range over the stored rows only: a dropped twin's or a dropped repeat book's sequence can fall outside them, so they do not bound expanded twins or source rows. A repeat book has the same timestamp, bids and asks as the same token's previous book in the hour's expanded stream, in (timestamp, sequence, row_ordinal) order, so a dropped repeat equals the last book of its token kept before it (a run of repeats all equal the book that starts it); the hour's rows stamped before hh:00 count, a book from another hour never does.",
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  "primary_rule_version": "primary-witness-1",
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      "provenance_columns": [
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    "timestamp_received": "millisecond, floored; not stored: timestamp + recv_lag_ms",
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  "sequence_note": "sequence and timestamp_received are collector-local provenance; where domains carry the same row, the merge keeps the PRIMARY witness's copy (p) when it heard the row, otherwise the EARLIEST-RECEIVED copy, equal arrivals breaking by registry order (active: i, o, p). Each row names its own in source_witness",
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