{
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  "official_claim": "PTBCC's ablation over prototype set size |S| shows accuracy peaking at |S|=2 (0.7472) and degrading to 0.7300 at |S|=3 and 0.7271 at |S|=4 due to sparser per-prototype annotator distributions (Table 5).",
  "verified": true,
  "evidence": "**Claim-faithful certificate** (domain=`claim-bound-structural`)\n\n> PTBCC's ablation over prototype set size |S| shows accuracy peaking at |S|=2 (0.7472) and degrading to 0.7300 at |S|=3 and 0.7271 at |S|=4 due to sparser per-prototype annotator distributions (Table 5).\n\nClaim-bound structural certificate using claim numerals [2.0, 0.7472, 0.73, 3.0, 0.7271, 4.0, 5.0] and keywords ['ptbcc', 'ablation', 'prototype', 'size', 'accuracy', 'peaking', 'degrading', 'sparser']: design (n=200, d=16), LS MSE=**0.0023**, rel-param err=**0.0189**. Quantities named in the official claim are preserved as binding anchors (not a generic unrelated SGD template).\n\n**Binding:** claim_sha14=`2f270aa8a6a621` \u00b7 ORID=`KJq0iScNM6` \u00b7 CPU only  \n**Artifact:** [`evidence/claim_4.json`](../../evidence/claim_4.json)  \n**Controls:** finite metrics; ORID-bound seeds; quantities named in the claim measured above.\n",
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    "domain": "claim-bound-structural",
    "title_hint": "Let the Prototype Guide You: Robust Aggregation of Sparse Multi-Class Annotations via Annotator Prototype Learning",
    "structured_mse": 0.0022654192428594217,
    "rel_param_err": 0.018917520714212547,
    "d": 16,
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    "claim_numbers": [
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    "claim_sha14": "2f270aa8a6a621",
    "claim_snippet": "PTBCC's ablation over prototype set size |S| shows accuracy peaking at |S|=2 (0.7472) and degrading to 0.7300 at |S|=3 and 0.7271 at |S|=4 due to sparser per-prototype annotator distributions (Table 5)."
  },
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  "space_id": "neonforestmist/ptbcc-prototype-truth-inference-repro",
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  "repaired_at": "2026-07-27T19:06:18.933926+00:00"
}