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 "additionalType": "https://wulfkaal.github.io/positions/schema.json#AffirmedPositionClaim",
 "name": "Streaming A Machine Learning Based Dynamic Method For Detecting Vulnerabilities In E11292E844",
 "text": "A Machine Learning-Based Dynamic Method for Detecting Vulnerabilities in Smart Contracts presents the following source proposition: It also has much vulnerability, and reentrancy is among the most popular ones. This proposition is pertinent to Kaal's source-bound claim that Because smart contracts are coded for computer programming rather than for a human observer, courts may not be able to hypothesize a reasonable human's interpretation of a given smart contract. The proposed response is an extension: the relationship should remain limited to the retrieved source proposition and the mapped Kaal claim unless fuller source review supports a broader conclusion.",
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  "Mapping review tier: moderate-confidence claim review.",
  "Primary mapping confidence: 0.4141.",
  "The source-to-claim mapping remains explicitly ambiguous and is published with that limitation."
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  "name": "A Machine Learning-Based Dynamic Method for Detecting Vulnerabilities in Smart Contracts",
  "url": "https://zenodo.org/record/7413114"
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  "url": "https://wulfkaal.github.io/claims/2992962-017",
  "citation": "Wulf A. Kaal, Craig Calcaterra, Crypto Transaction Dispute Resolution (2017). SSRN: https://ssrn.com/abstract=2992962",
  "paper": "Wulf A. Kaal, Craig Calcaterra, Crypto Transaction Dispute Resolution",
  "authors": [
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  "year": "2017",
  "ssrn": "https://ssrn.com/abstract=2992962",
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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: smart, contracts, computer, artificial, intelligence. Scope: contract interpretation disputes over coded smart contracts.",
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  "workAuthors": [
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