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
3Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2163480927
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Amelie Wührl (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Innovation: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Output Quality: 1 paper
- Task Allocation: 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 human-centric attribution framework would let creators, users and platforms negotiate when and how LLM outputs should be traced to training data, clarifying ownership and helping prevent unknowingly copied content; its success, however, hinges on technical feasibility and cross-stakeholder incentives.openalex | Amelie Wührl provider id |
2026-02-11 | 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.