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:
2289692208
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Ema Smolic (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Error Rate: 1 paper
- Ai Safety And Ethics: 1 paper
- Developer Productivity: 1 paper
- Organizational Efficiency: 1 paper
- Output Quality: 1 paper
- Training Effectiveness: 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 |
|---|---|---|---|
| AI coding models generated thousands of reliable unit tests in hours and substantially reduced regression risk during major refactors; supervised, iterative test generation constrained model-driven code changes and cut manual labor for a prototype codebase.arxiv | Ema Smolic provider id |
2026-04-03 | 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.