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:
2457502685
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Song Yao (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Labor Markets: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Skill Obsolescence: 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 |
|---|---|---|---|
| Kaggle medals remain informative but perishable: almost all predictive power sits in the first year, and the apparent collapse of upload-based credentials after the platform phased out upload evaluations is mainly a consequence of a frozen medal stock aging, not clear evidence that individuals became less skilled under AI.arxiv | Song Yao provider id |
2026-08-17 | 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.