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 "name": "Historical 37143F9Fbfbd62D5C869",
 "text": "Blockchain Integration for Trust and Consent Management in MCP-Enabled FHIR Systems should be assessed against Kaal's source-bound claim that Because blockchain records a verifiable and immutable history of data provenance and alterations, it mitigates data poisoning risk and supports the claim that AI models were trained on genuine datasets. The current metadata indicates a plausible connection through model context protocol, but the defensible response is a qualification until the source text confirms agreement, scope, methods, and limitations.",
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  "identifier": "https://orcid.org/0009-0008-7840-1847"
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  "External evidence level: abstract indexed.",
  "Mapping review tier: ambiguity triage before claim review.",
  "The literature-to-claim mapping remains explicitly ambiguous and should not be treated as a settled equivalence."
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  "url": "https://www.semanticscholar.org/paper/0f9b9fe3503319ee8a35a7bf11313e48ba71f9f7"
 },
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  "url": "https://wulfkaal.github.io/claims/5245185-003",
  "citation": "Wulf A. Kaal, How can we Best Monitor AI Agents (2025). SSRN: https://ssrn.com/abstract=5245185",
  "paper": "How can we Best Monitor AI Agents",
  "authors": [
   "Wulf A. Kaal"
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  "year": "2025",
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 "mappingAmbiguous": true,
 "mappingMethod": "idf-weighted multi-field mapping v1",
 "mappingWhyRelevant": "Shared high-information concepts: blockchain, trust, provenance, verifiable, risks. Scope: training data recorded on chain; systems relying on large language models.",
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  "source": "Semantic Scholar",
  "api": "https://api.semanticscholar.org/graph/v1/paper/search/bulk",
  "query": "model context protocol",
  "queryId": "concept:9c5da690faad",
  "page": 1,
  "sourceRank": 63,
  "retrievedAt": "2026-07-31T13:58:04.059Z",
  "citationCount": 0,
  "venue": "International Conference on Deep Learning Technologies",
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