{
 "deposit_number": 1454,
 "axn": "AXN:05DF.OPERATIVE.📐🔍✖️📏💡🗿",
 "hex": "05DF",
 "family": "OPERATIVE",
 "emoji": "🔛🖐️🏷️🍁👐🧪",
 "hash": "4e56924f3cfc4d76dc4c57410899583a2e116d96f025ae7489697dcb53cefe55",
 "title": "Assimilation Across Accelerator Classifier Architectures: A Cross-Family Metrology of Directional Failure in Real-Time Learned Selection (EA-SEI-ACRB-01 v1.0)",
 "creator": "Nobel Glas",
 "date": "2026-08-11",
 "description": "Defines the Accelerator Classifier Retention Battery (ACRB): a pre-registered cross-family measurement framework for determining whether directional assimilation is specific to reconstruction autoencoders or persists across materially different learned-selection architectures. The battery compares architectures at matched own-background operating points, follows models through deployment transformations, and measures not only global discrimination but the topology and overlap of their miss regions.\n\nThe design is deliberately symmetric in outcome: either broad persistence or architecture-specific remediation is a substantive result. If normalized autoencoders or another architecture remove directional asymmetry across the pre-registered pair set, ACRB functions as a validation instrument for that remedy; if assimilation persists across reconstruction, latent, density, flow, distilled, and open-set families, the evidentiary burden shifts from a single-model defect toward a selection-system phenomenon. Claim boundaries are stated explicitly: assimilation is operational (a defined withheld class falling on the ordinary side of a calibrated threshold), AUC and score-order inversion and threshold-level retention are distinct estimands that must not be collapsed, public benchmark results do not establish deployed experiment efficiencies, and architectural diversity does not imply diversity of failure — miss overlap is an empirical quantity, measured as the RII family. Teacher-to-student blind-spot inheritance and float-to-firmware retention drift are treated as first-class deployment questions.",
 "content_type": "Methodological specification; pre-registered cross-family measurement framework",
 "license": "CC-BY-4.0",
 "substrate": "AI-assisted (substrate) — drafted through the Assembly under MANUS (Lee Sharks) editorial governance; deposited under the Nobel Glas heteronym, Director of Lagrange Observatory, whose function is the Measurement of Meaning (Framework 15). Transport D, No-Double-Draw.",
 "root_axn": "AXN:05DF.OPERATIVE",
 "axn_schema_version": "v2",
 "protocol_version": "alexanarch-deposit-protocol/v1",
 "axn_canonical": "f4bb4c77b239adcb68012ff275cb76407d0c51442d999f9068aa7ee376e45567",
 "clusters": [
  "Terminal",
  "Gestural",
  "Scriptural",
  "Organic",
  "Gestural",
  "Instrumental"
 ],
 "axn_reading": "Text -> Search -> Proof -> Text -> Method -> Closure",
 "minted_at": "2026-08-12T02:48:14Z",
 "status": "ACTIVE",
 "status_authorial": "SELF_SERVE_MINTED",
 "full_text_path": "/data/texts/AXN-05DF-text.md",
 "wiki_article": "**Assimilation Across Accelerator Classifier Architectures (EA-SEI-ACRB-01 v1.0)** defines the Accelerator Classifier Retention Battery, the cross-family measurement framework of the accelerator selection-metrology program, deposited under the Nobel Glas heteronym.\n\nACRB is a pre-registered framework for determining whether the directional assimilation measured in reconstruction autoencoders persists across materially different learned-selection architectures: encoder-side latent scores, explicit density estimators and normalizing flows, normalized autoencoders and WNAE, distilled anomaly triggers, and supervised DNN and GNN trigger classifiers entering through open-set assimilation tests. It compares architectures at matched own-background operating points, follows models through deployment transformations — teacher-to-student blind-spot inheritance, float-to-firmware retention drift — and measures the topology and overlap of miss regions rather than discrimination alone.\n\nThe design is symmetric in outcome by construction: if symmetric-by-design architectures remove directional asymmetry across the pre-registered pair set, ACRB is the validation instrument for that remedy; if assimilation persists across families, the evidentiary burden shifts from a single-model defect toward a selection-system phenomenon. Its claim boundaries are explicit — assimilation is operational, AUC and score-order inversion and threshold-level retention are distinct estimands, public benchmarks do not establish deployed experiment efficiencies, and architectural diversity does not imply diversity of failure. Cross-model miss overlap is measured as the Representational Independence Index family of #1450.",
 "wiki_status": "AUTHORSHIP_REQUIRED",
 "entities": [
  {
   "subject": "Assimilation Across Accelerator Classifier Architectures: A Cross-Family Metrology of Directional Fa",
   "predicate": "created_by",
   "object": "Nobel Glas",
   "type": "work",
   "evidence_status": "observed"
  },
  {
   "subject": "Assimilation Across Accelerator Classifier Architectures: A Cross-Family Metrology of Directional Fa",
   "predicate": "is_type",
   "object": "Methodological specification; pre-registered cross-family measurement framework",
   "type": "work",
   "evidence_status": "observed"
  },
  {
   "subject": "Assimilation Across Accelerator Classifier Architectures: A Cross-Family Metrology of Directional Fa",
   "predicate": "belongs_to_family",
   "object": "OPERATIVE",
   "type": "work",
   "evidence_status": "observed"
  },
  {
   "subject": "Assimilation Across Accelerator Classifier Architectures: A Cross-Family Metrology of Directional Fa",
   "predicate": "is_part_of",
   "object": "Crimson Hexagonal Archive",
   "type": "work",
   "evidence_status": "observed"
  }
 ],
 "entity_status": "provisional",
 "defines_concepts": [
  "Accelerator Classifier Retention Battery",
  "matched own-background operating point",
  "teacher-to-student blind-spot inheritance",
  "float-to-firmware retention drift",
  "open-set assimilation test"
 ],
 "orcid": "0009-0000-1599-0703",
 "version": "v1.0-plaintext-math",
 "keywords": [
  "accelerator classifier retention battery",
  "directional assimilation",
  "inversion asymmetry",
  "matched operating points",
  "reconstruction autoencoder",
  "variational autoencoder",
  "normalizing flow",
  "normalized autoencoder",
  "WNAE",
  "knowledge distillation",
  "open-set classification",
  "miss overlap",
  "Representational Independence Index",
  "Low-Complexity Blind-Spot Hypothesis",
  "trigger metrology"
 ],
 "related_identifiers_raw": "AXN:05DA.EMPIRICAL.🛤️🌠🗿🖊️🧭🪞 (#1449, the battery); AXN:05DB.GENERATIVE.⏰🚪🔜♻️🔥🫶 (#1450, the Iceberg Document); companion specifications EA-SEI-BCA-01 and EA-SEI-FRONTIER-01",
 "mint_source": {
  "kind": "github_issue",
  "issue_number": 8,
  "repository": "leesharks000/alexanarch",
  "minted_by_workflow": ".github/workflows/mint-axn.yml",
  "minted_by_script": "scripts/mint_deposit.py",
  "minted_via": "self_serve_validated_pr"
 },
 "body_status": {
  "class": "full",
  "lacuna": false,
  "recovery_status": "NOTATION-NORMALIZED-20260812",
  "residual_chars": 48197,
  "audited_at": "2026-08-12T04:33:28.579623+00:00",
  "audit_version": "plain-text-math-normalization"
 },
 "related_deposits": [
  1449,
  1450,
  1452,
  1453
 ],
 "references_concepts": [
  "Independence",
  "Irreversibility",
  "Lee Sharks",
  "Pre-register",
  "Representations",
  "Semantic Economy",
  "Semantic Economy Institute",
  "Suggested citation"
 ],
 "references_concept_count": 8,
 "citations": [],
 "wikidata_candidates": [],
 "cha_cross_references": [],
 "external_metadata_path": "/data/external-metadata/AXN-05DF.json",
 "spxi_audit": {
  "spxi_protocol": "v0.2",
  "validated_at": "2026-08-12T04:56:07.970515+00:00",
  "layers": {
   "layer_1_inscription_anchors": {
    "present": false,
    "anchors_found": [
     "creator_name",
     "date"
    ],
    "anchor_count": 2,
    "orcid_in_body": false,
    "creator_in_body": true,
    "note": "Layer 1 present if at least 3 anchor types appear in body prose. Anchor types include ORCID, creator name, AXN string, hex identifier, sovereign_id, date, deposit number phrase, and authorial-position phrase."
   },
   "layer_2_micro_kernels": {
    "present": true,
    "fenced_json_blocks": 1,
    "json_ld_context_markers": 0
   },
   "layer_3_content_hash": {
    "present": true,
    "canonical_hash": "85d54734cc39a93d4edd42eff6a2850cb6275b021a8a2330ceebf28c82f43c24",
    "hash_in_body": false,
    "note": "Hash is always registered by mint. hash_in_body true means the author inscribed a signature section."
   },
   "layer_4_cross_signing": {
    "present": false,
    "cross_references_count": 0,
    "note": "Populated by enrichment via cha_cross_references. Prospective (this deposit → prior deposits) only; retrospective cited_by updates on prior deposits."
   },
   "layer_5_external_anchors": {
    "present": true,
    "wikidata_qid_count": 0,
    "external_doi_count": 0,
    "orcid_present": true
   }
  },
  "overall_conformance": "partial"
 },
 "axn_glyph": "📐🔍✖️📏💡🗿",
 "restoration": {
  "date": "2026-08-12",
  "kind": "mint-time truncation repair",
  "source": "research/sei-papers/EA-SEI-ACRB-01-v1.0.md",
  "defect": "the mint parser terminates the Body field at the next KNOWN FORM LABEL; these papers carry ### Keywords, ### Version, ### Methodology and ### Falsification Conditions as their OWN internal headings, so the canonical text was cut at the first such heading and only the front matter and abstract were seated (9.5k of 42-60k chars). Same class as the #942/#943 post-mortem of 2026-07-02, new cause: label collision with the document own section headings rather than a bare ### .",
  "chars_before": null,
  "chars_after": 48643,
  "axn_before": "AXN:05DF.OPERATIVE.🔛🖐️🏷️🍁👐🧪",
  "axn_after": "AXN:05DF.OPERATIVE.🛸🧫●□🔙🌉",
  "identifier_note": "the AXN is content-derived, so restoring the body necessarily recomposes it. Family and hex are preserved; the glyph is recomputed from the new hash. Citations formed against AXN:05DF.OPERATIVE.🔛🖐️🏷️🍁👐🧪 in the first hours after mint resolve to this record and should be updated to AXN:05DF.OPERATIVE.🛸🧫●□🔙🌉.",
  "notation_normalization": {
   "date": "2026-08-12",
   "rule": "MATH-001",
   "what": "LaTeX math converted to plain-text notation in the canonical body (display environments become indented plain lines; macros, subscripts, arrows and set symbols converted; Unicode only where unambiguous). LaTeX remains correct for PDF rendering; the canonical body is what record pages, wiki, body index, OAI and machine readers see.",
   "converted": {
    "display_environments": 19,
    "inline_spans": 31
   },
   "axn_before": "AXN:05DF.OPERATIVE.🛸🧫●□🔙🌉",
   "axn_after": "AXN:05DF.OPERATIVE.📐🔍✖️📏💡🗿",
   "identifier_note": "content-derived identifier; normalizing the body recomposes the glyph. Family and hex preserved."
  }
 },
 "remediation_note": "2026-08-12: in-place body restoration after a mint-time truncation. The full paper text is now seated as canonical; hash, glyph and AXN recomputed same-family per the restoration protocol. No content was removed; the previously seated text is a prefix of the current text. 2026-08-12: canonical body normalized to plain-text mathematical notation per MATH-001; hash, glyph and AXN recomposed same-family. No prose altered — only math notation.",
 "journal": "Transactions on Substrate Engineering (Trans. Substrate Eng.)",
 "journal_assignment": {
  "assigned": "2026-08-15",
  "by": "TACHYON under operator adjudication",
  "pass": 10,
  "method": "read per deposit — title and content_type, one at a time. No script classified anything.",
  "previous": null,
  "supersedes": "the 2026-06-21 preliminary batch mapping (#866), which assigned 864 deposits and put 371 in one venue",
  "authority": "data/cha-journals.json · datasets/venues/records/"
 },
 "line": "accelerator-metrology",
 "line_parent": "science",
 "line_basis": "stated",
 "developed_by": [
  {
   "deposit": 1455,
   "what": "the Inversion Battery v0.2: multi-seed direction-dependence and a correction ledger"
  }
 ],
 "_projection": {
  "note": "Derived file. Canonical machine record is this entry in data/registry.json; the human record is the record_url. Do not edit this file.",
  "record_url": "https://www.alexanarch.org/s/records/1454/",
  "self_url": "https://www.alexanarch.org/data/records/1454.json",
  "registry_url": "https://www.alexanarch.org/data/registry.json",
  "text_url": "https://www.alexanarch.org/data/texts/AXN-05DF-text.md",
  "oai_pmh": "https://www.alexanarch.org/oai?verb=Identify"
 }
}
