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
2Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2404913826
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Чуньин Чэ (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Error Rate: 1 paper
- Task Completion Time: 1 paper
- Governance And Regulation: 1 paper
- Skill Acquisition: 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 |
|---|---|---|---|
| Neural translation speeds up routine work but creates a new validation economy: AI improves formal correctness and throughput for standard texts but increases semantic and stylistic failures that raise cognitive costs on complex material, while post-editing and focused training restore quality and shift translators into expert validators.openalex | Чуньин Чэ provider id |
2025-12-30 | 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.