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
6Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2435770067
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Samanata Silwal (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Decision Quality: 1 paper
- Output Quality: 1 paper
- Research Productivity: 1 paper
- Regulatory Compliance: 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 |
|---|---|---|---|
| Commercial LLMs speak like junior engineers but often mislead: over half of cited sources are unverifiable and model confidence is inversely related to reasoning quality, producing alignment with experts for operational triage but near-complete divergence on strategic capital decisions.openalex | Samanata Silwal provider id |
2026-05-14 | 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.