Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
7Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2344756464
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Otter Quarks (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Innovation: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Ai Safety And Ethics: 1 paper
- Firm Revenue: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| A practical six-step framework translates AI safety scenarios into quantitative economic risk estimates, enabling probabilistic claims such as the likelihood of exceeding $Y in annual cyber damages; the approach adapts established risk-management tools and is illustrated via LLM-enabled cyber offense (empirical results reported separately).arxiv | Otter Quarks provider id |
2025-12-09 | 2 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 2 cumulative citations. This is a coverage summary, not an author score or h-index.