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:
2153527806
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Hengwei Zhang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Error Rate: 1 paper
- Research Productivity: 1 paper
- Decision Quality: 1 paper
- Firm Revenue: 1 paper
- Organizational Efficiency: 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 new estimation framework (GAI) uses LLM outputs as auxiliary information to shrink human-label needs and tighten inference: theory guarantees weakly better efficiency than human-only estimators and applied tests show up to ~90% reductions in labeling while preserving decision accuracy; gains are largest when AI signals are predictive but the method is a safe default even with weak auxiliary data.arxiv | Hengwei Zhang provider id |
2026-04-16 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.