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    {
      "sequence": 37670,
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      "action": "LATTICE_ANCHOR",
      "payload": {
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        "kind": "aziel-corpus.verified_ingest",
        "library": "aziel",
        "record_id": "AZDOC-D409A74F7D77",
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      "action": "ZSOLVER_SCORE",
      "payload": {
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        "capped_confidence": null,
        "display": null,
        "provisional": true,
        "record_id": "AZDOC-D409A74F7D77",
        "seed_corpus": false,
        "separate_from_triad": true,
        "source": "not_applicable",
        "status": "not_applicable"
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    {
      "sequence": 37672,
      "timestamp_utc": "2026-09-18T01:49:11.312Z",
      "action": "JSON_DISCOVERY",
      "payload": {
        "action": "JSON_DISCOVERY",
        "content_sha256": "3f4cac06494abb43f91bed1c0029532b5296d0b8b2c643bed1234086075279b1",
        "json_tree_object_key": ".Json/JSONAZDOC-D409A74F7D77.json",
        "lattice_kind": "aziel-corpus.json_discovery",
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        "law": "adaptive learning via hashchain lattice for recollection and reasoning",
        "map4d": {
          "axes": {
            "Delta": "interval between pins",
            "Gamma": "trajectory across pins",
            "Pi": "pattern support / contradiction density",
            "T": "clock / paper event_date"
          },
          "cite": "https://github.com/AzielEliab/4dmap",
          "home": "https://4dmap-download-tracker.vibelock.workers.dev/",
          "note": "Inspection frame after AZPIPE, not an extra door. Not a live ICANN mesh DNS. Not GIS 4D. Receipts are not truth.",
          "spec": "4DM-WP-1.0"
        },
        "note": "Pins are paper date × event × geolocation. Never upload time.",
        "ok": true,
        "pinned": 0,
        "record_id": "AZDOC-D409A74F7D77",
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      "timestamp_utc": "2026-09-18T01:49:36.288Z",
      "action": "LEARN",
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        "author": "Aziel Eliab",
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        "features": null,
        "kind": "accept",
        "law": "adaptive learning via hashchain lattice for recollection and reasoning",
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        "possibility_kind": "HEURISTIC",
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        "kind": "HEURISTIC",
        "law": "adaptive learning via hashchain lattice for recollection and reasoning",
        "not_truth": true,
        "note": "possibility ≠ probability ≠ triad ≠ ZionPattern. HEURISTIC density over lattice pin receipts. Not Beta-Bernoulli. Not a guilt verdict. Not courtroom proof. Posterior ≠ truth.",
        "possibility": null,
        "record_id": "AZDOC-D409A74F7D77",
        "refuse": "NO_ANCHORS",
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      "action": "STRUCTURE_VERIFY",
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      "action": "LATTICE_ANCHOR",
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      "action": "LATTICE_ANCHOR",
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        },
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    {
      "sequence": 49721,
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      "action": "LATTICE_ANCHOR",
      "payload": {
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      "action": "STRUCTURE_VERIFY",
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        "file_count": 1,
        "library": "aziel",
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  "body": "CNLT-1: Canonical Normalized Lexical Transcription v1\n22 Aug 2026\n\nPURPOSE\nCNLT-1 is a structural proto-transcription, not plaintext. It rewrites each RF1b/CFST-1 token as:\nL=<left physical operator> | LEMMA=<anonymous invariant body ID>[<body>] | R=<right physical operator> | STATE=<5-state grammar class>.\n\nEVIDENCE FOR THE LEMMA LAYER\n1. Internal family-body sequences agree between directly aligned ZL and GC tokens slightly more often than complete family sequences.\n2. Across ZL, GC and RF, different edge-marked surface forms sharing the same internal body have significantly more similar non-adjacent within-line body contexts than frequency- and ending-state-matched forms with different bodies.\n3. This direction holds in 15/15 page-group folds.\n4. This supports treating the body as a lemma-like invariant for structural analysis. It does NOT establish pronunciation, language, or meaning.\n\nCONTEXT INVARIANCE TEST\n- Tokens: simple STA family sequences of length >=3.\n- Body: token sequence after removing one physical family at each edge.\n- Surface variants: distinct complete family sequences sharing a body.\n- Context: bodies at token distance 2..5 within the same physical line; immediately adjacent tokens excluded to avoid simply re-measuring the known edge grammar.\n- Metric: Jensen-Shannon divergence of context distributions.\n- Control: different-body surface forms matched approximately on frequency and final-family/state class.\n- Full corpus: one-sided paired Wilcoxon across eligible body families.\n- Robustness: five page-group partitions in each ZL/GC/RF corpus.\n\nINTERPRETATION\nCNLT-1 is the closest supported normalized transcription currently available in this project. Anonymous lemma IDs are deliberately semantic-free.\n\n\nAziel Eliab\nresearch, philosophy, Voynich\nCNLT1 Canonical Normalized Lexical Transcription — README\nABAD, Voynich, CNLT1, Aziel",
  "author": "Aziel Eliab",
  "domain": "research, philosophy, Voynich",
  "subjects": "CNLT1 Canonical Normalized Lexical Transcription — README",
  "keywords": "ABAD, Voynich, CNLT1, Aziel"
}