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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2385941983
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Janis Purk (semantic scholar, provider refresh)
- Janis Purk (semantic scholar, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Adoption Rate: 1 paper
- Error Rate: 1 paper
- Governance And Regulation: 1 paper
- Other: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| Human-led prompt calibration markedly improves LLM supply‑chain alerts: embedding domain anchors and company context reduces central‑tendency bias and yields better-aligned risk scores with experts, though gains are validated only on a limited pilot sample.semantic_scholar | Janis Purk provider id |
2026-07-27 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.