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 "text": "Facilitating AI-Driven Sustainability: A Service-Oriented Architecture for Interoperable Environmental Data Access should be assessed against Kaal's source-bound claim that The governance protocols required for GDPR and AI Act compliance, including anonymization, data minimization, and explicit consent, themselves complicate the assembly of robust AI training 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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  "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/01a8eecf8565ab9d7b9cfdc17bf41b188bfade45"
 },
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  "identifier": "kaal:claim:5095633-012",
  "url": "https://wulfkaal.github.io/claims/5095633-012",
  "citation": "Wulf A. Kaal, Artificial Intelligence The Final Frontier (2025). SSRN: https://ssrn.com/abstract=5095633",
  "paper": "Artificial Intelligence The Final Frontier",
  "authors": [
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  "year": "2025",
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 "recordTypeNote": "Dated commentary position extending a scholarly corpus claim. Not a verbatim claim extracted from the paper.",
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 "mappingWhyRelevant": "Shared high-information concepts: data, artificial, intelligence, however, required, agents, robust, compliance. Scope: jurisdictions with GDPR-style data protection mandates.",
 "sourceProvenance": {
  "source": "Semantic Scholar",
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  "query": "model context protocol",
  "queryId": "concept:9c5da690faad",
  "page": 1,
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  "retrievedAt": "2026-07-31T13:58:04.059Z",
  "citationCount": 0,
  "venue": "Sustainability",
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