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:
2368458901
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Christian Kastner (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
- Research Productivity: 1 paper
- Organizational Efficiency: 1 paper
- Training Effectiveness: 1 paper
- Other: 1 paper
- Task Allocation: 1 paper
- Task Completion Time: 1 paper
- Team Performance: 1 paper
- Worker Satisfaction: 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 agents change the dynamics of code review but do not unilaterally improve outcomes; whether agent-written code helps or harms depends on how teams structure and staff review, according to a large-scale analysis of GitHub activity and 3,100 coded practitioner discussions.arxiv | Christian Kastner provider id |
2026-07-08 | 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.