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
29Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2209707528
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Souvik Bhattacharya (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Adoption Rate: 1 paper
- Developer Productivity: 1 paper
- Error Rate: 1 paper
- Organizational Efficiency: 1 paper
- Task Completion Time: 1 paper
- Automation Exposure: 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 |
|---|---|---|---|
| Risk-stratified automated review absorbed much of Meta’s AI-driven code surge: RADAR landed 331K of 535K+ reviewed changes, cut median time-to-close and review wall time sharply, and recorded far lower revert and production-incident rates than non-automated diffs.arxiv | Souvik Bhattacharya provider id |
2026-05-28 | 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.