kaal:position:2026-07-31-6728

Ascertaining Susceptibilities in Smart Contracts: A Quantum Machine Learning Approach presents the following source proposition: But liabilities in smart contracts could result in unfamiliar system failures. 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.

• The response is limited to the retrieved source proposition and mapped Kaal claim unless fuller source review supports a broader conclusion.
• External evidence level: abstract indexed.
• Mapping review tier: moderate-confidence claim review.
• Primary mapping confidence: 0.3759.
• The source-to-claim mapping remains explicitly ambiguous and is published with that limitation.

Ascertaining Susceptibilities in Smart Contracts: A Quantum Machine Learning Approach

kaal:claim:2992962-017
Wulf A. Kaal, Craig Calcaterra, Crypto Transaction Dispute Resolution (2017). SSRN: https://ssrn.com/abstract=2992962
Source PDF sha256: 80b92e67594394e38af41b769327936833de8e25d6cf3def966a6ba13bc83d17

Evidence: abstract indexed
Review tier: moderate-confidence claim review
Mapping confidence: 0.3759
Mapping ambiguous: true

law-and-legal-systemssmart-contractshistorical-responsescholarly-literatureopenalex

Affirmed in kaal-review:2026-07-31:streaming-etl-0005 on 2026-07-31. Review record.

Canonical markdown sha256: 16416a4b1e88c9280e8bfa1b1c16cdc135dfd693c69d7d2d9578b8253888b621
curl -s https://wulfkaal.github.io/positions/2026-07-31-6728.md | sha256sum