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
1Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
No provider ID is stored.
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Certified SAFe® 6 Agilist, New York, United States (openalex, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
Claim outcomes
- Firm Productivity: 1 paper
- Other: 1 paper
- Error Rate: 1 paper
- Adoption Rate: 1 paper
- Output Quality: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex 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 |
|---|---|---|---|
| AI analytics materially sharpen group-insurance risk forecasts: gradient boosting and hybrid models raised discrimination for high-cost events from 0.74 to 0.87 and cut sponsor loss-ratio error from 0.084 to 0.057, reducing reserve bias and compressing volatility; these gains persist in out-of-sample, forward-renewal and industry-shift tests.openalex | Certified SAFe® 6 Agilist, New York, United States unresolved |
2025-12-01 | 1 |
Citation observation summary
OpenAlex supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.