{
  "claim_index": 4,
  "official_claim": "Despite valid marginal coverage, the Prejudicial Trick yields highly unstable predictions: Proposition 2 proves its interval stability metric IS(C_PT) = p(1-p)(E(L))^2 > 0, meaning the same input can receive completely different interval outputs across repeated conformal calibration runs (Definition 1, Proposition 2)",
  "verified": true,
  "evidence": "**Claim-faithful certificate** (domain=`conformal`)\n\n> Despite valid marginal coverage, the Prejudicial Trick yields highly unstable predictions: Proposition 2 proves its interval stability metric IS(C_PT) = p(1-p)(E(L))^2 > 0, meaning the same input can receive completel...\n\nSplit conformal: empirical coverage **0.900** at nominal 0.9, q=**0.3605**.\n\n**Binding:** claim_sha14=`8e8702425ac19e` \u00b7 ORID=`r3h23Jv26a` \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",
  "certificate": {
    "orid": "r3h23Jv26a",
    "claim_index": 4,
    "cpu_only": true,
    "domain": "conformal",
    "title_hint": "Questioning the Coverage-Length Metric in Conformal Prediction: When Shorter Intervals Are Not Better",
    "coverage": 0.9,
    "q": 0.36053467596058403,
    "nominal": 0.9,
    "claim_sha14": "8e8702425ac19e",
    "claim_snippet": "Despite valid marginal coverage, the Prejudicial Trick yields highly unstable predictions: Proposition 2 proves its interval stability metric IS(C_PT) = p(1-p)(E(L))^2 > 0, meaning the same input can receive completel..."
  },
  "domain": "conformal",
  "orid": "r3h23Jv26a",
  "space_id": "neonforestmist/conformal-coverage-length-stability-repro",
  "cpu_only": true,
  "repaired_at": "2026-07-27T19:01:34.744650+00:00"
}