{
 "@context": "https://schema.org",
 "@type": "Claim",
 "@id": "https://wulfkaal.github.io/positions/2026-07-31-4236",
 "identifier": "kaal:position:2026-07-31-4236",
 "additionalType": "https://wulfkaal.github.io/positions/schema.json#AffirmedPositionClaim",
 "name": "Historical 147Cf45Dadcacecf5Ef2",
 "text": "Toward Responsible AI in High-Stakes Domains: A Dataset for Building Static Analysis with LLMs in Structural Engineering should be assessed against Kaal's source-bound claim that WDAGs allow new regulatory and ethical standards to be integrated into existing AI systems without overhauling the entire model architecture, which is what makes rapid legal adaptation feasible in sectors such as public safety and healthcare. 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.",
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
 },
 "datePublished": "2026-07-31",
 "dateModified": "2026-07-31",
 "creativeWorkStatus": "Affirmed",
 "responseType": "qualification",
 "keywords": [
  "compliance",
  "ai-and-agents"
 ],
 "scope_conditions": [
  "sectors where AI applications must rapidly adapt to new laws and ethical considerations",
  "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."
 ],
 "currentDebate": {
  "name": "Toward Responsible AI in High-Stakes Domains: A Dataset for Building Static Analysis with LLMs in Structural Engineering",
  "url": "https://www.semanticscholar.org/paper/fff649e2c89b804a336d4ee90765d3a754f286d5"
 },
 "extends": {
  "identifier": "kaal:claim:4855607-024",
  "url": "https://wulfkaal.github.io/claims/4855607-024",
  "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
  "paper": "How AI Models are Optimized Through Web3 Governance",
  "authors": [
   "Wulf A. Kaal"
  ],
  "year": "2024",
  "ssrn": "https://ssrn.com/abstract=4855607",
  "source_pdf_sha256": "eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113"
 },
 "isBasedOn": [
  {
   "@id": "https://wulfkaal.github.io/claims/4855607-024"
  },
  {
   "@type": "CreativeWork",
   "name": "Toward Responsible AI in High-Stakes Domains: A Dataset for Building Static Analysis with LLMs in Structural Engineering",
   "url": "https://www.semanticscholar.org/paper/fff649e2c89b804a336d4ee90765d3a754f286d5"
  }
 ],
 "batch_id": "historical-backfill:2026-07-31:phase-0017",
 "review_provenance": "https://kaal-signal-desk.wulf577462.chatgpt.site/#review",
 "publicationStatus": "public",
 "recordTypeNote": "Dated commentary position extending a scholarly corpus claim. Not a verbatim claim extracted from the paper.",
 "isPartOf": {
  "@id": "https://wulfkaal.github.io/positions/index.json"
 },
 "version": "1.0",
 "canonical_url": "https://wulfkaal.github.io/positions/2026-07-31-4236",
 "canonicalForm": "https://wulfkaal.github.io/positions/2026-07-31-4236.md",
 "candidateId": "kaal:response-candidate:2026-07-31:f26a1045da1b7ad1",
 "evidenceLevel": "abstract indexed",
 "reviewTier": "ambiguity triage before claim review",
 "mappingConfidence": 0.2204,
 "mappingAmbiguous": true,
 "mappingMethod": "idf-weighted multi-field mapping v1",
 "mappingWhyRelevant": "Shared high-information concepts: such, safety, compliance, through, integration, architecture, standards. Scope: sectors where AI applications must rapidly adapt to new laws and ethical considerations.",
 "sourceProvenance": {
  "source": "Semantic Scholar",
  "api": "https://api.semanticscholar.org/graph/v1/paper/search/bulk",
  "query": "model context protocol",
  "queryId": "concept:9c5da690faad",
  "page": 2,
  "sourceRank": 1016,
  "retrievedAt": "2026-07-31T13:58:04.528Z",
  "citationCount": 4,
  "venue": "International Conference on Data Technologies and Applications",
  "publicationTypes": [
   "JournalArticle"
  ]
 },
 "userAffirmation": "Approved as written by Wulf A. Kaal on 2026-07-31.",
 "sha256": "e63fd04f97b52a39d24d44f39bacf060e034c0c60ac5714cea7e5030daa85f2b"
}