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
5Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
5090160
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- J. Llull (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
Claim outcomes
- Research Productivity: 1 paper
- Ai Safety And Ethics: 1 paper
- Adoption Rate: 1 paper
- Decision Quality: 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 |
|---|---|---|---|
| Language models do not produce fixed measurements: sampling, hidden updates, floating-point effects and expert routing make LLM outputs variable and able to change empirical results; researchers should record model identifiers, timestamps, seeds when available, and provide multiple draws and replication materials so analyses can be assessed and reproduced.arxiv | J. Llull provider id |
2026-07-27 | 1 |
Citation observation summary
Semantic Scholar 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.