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
1Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5124065236
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Qingjin Peng (openalex, 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
- Skills Training: 1 paper
Claim outcomes
- Other: 1 paper
- Task Completion Time: 1 paper
- Worker Satisfaction: 1 paper
- Adoption Rate: 1 paper
- Decision Quality: 1 paper
- Error Rate: 1 paper
- Output Quality: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex 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 |
|---|---|---|---|
| An augmented-reality system driven by a multimodal LLM cuts CMM task time and perceived workload while preserving measurement accuracy, enabling less-expert operators to complete precision measurement tasks faster; the result suggests productivity and training-cost gains in precision manufacturing, but evidence is limited to a single-machine case study and modest sample.openalex | Qingjin Peng provider id |
2026-03-05 | 0 |
Citation observation summary
OpenAlex 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.