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 "identifier": "kaal:entity:synthetic-data",
 "name": "Synthetic data",
 "termCode": "synthetic-data",
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 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
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   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/5095633-011",
   "identifier": "kaal:claim:5095633-011",
   "text": "As AI-generated content proliferates online it dilutes the diversity and originality of the text pool available for later training, producing performance degradation across successive model generations.",
   "abstract": "As AI-generated content proliferates online, it dilutes the overall diversity and originality of text available for subsequent training processes, potentially leading to a degradation in model performance over repeated generations.",
   "citation": "Wulf A. Kaal, Artificial Intelligence The Final Frontier (2025). SSRN: https://ssrn.com/abstract=5095633",
   "datePublished": "2025",
   "claim_type": "mechanism",
   "confidence": "evidenced",
   "is_failure_mode": true,
   "scope_conditions": [
    "repeated training generations drawing on an increasingly synthetic web"
   ],
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   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/7598518-015",
   "identifier": "kaal:claim:7598518-015",
   "text": "Feng et al. (2025) show that verifier-selected synthetic data can prevent collapse, and the paper concedes the core of that result: verifier selection is an established remedy, and the oracle ceiling and the Gaussian setting are established.",
   "abstract": "Intelligence models increasingly learn from data that earlier Intelligence models produced, and when that loop repeats the rare cases disappear first. This paper preregisters a test of whether a reputation-weighted validator pool, filtering the training data of a recursive replacement loop, keeps those rare cases alive. The outcomes remain sealed until a replication of the original model-collapse result succeeds.",
   "citation": "Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). SSRN: https://ssrn.com/abstract=7598518",
   "datePublished": "2026",
   "claim_type": "condition",
   "confidence": "reported",
   "is_failure_mode": false,
   "scope_conditions": [
    "The result is prior work, not a finding of this study.",
    "What this paper isolates is how to weight several assessors behind a validity filter, not whether verifier selection works."
   ],
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 "description": "2 claims in the published works of Wulf A. Kaal carry the concept tag 'synthetic-data'. Derived node: a roster, not an adjudicated definition."
}