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
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2452641870
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Korota Arsène Coulibaly (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Research Productivity: 1 paper
- Adoption Rate: 1 paper
- Innovation Output: 1 paper
- Organizational Efficiency: 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 survey of 547 papers shows multi-agent digital twins advance predictive maintenance but fall short of an integrated, on-device-to-distributed hierarchical architecture; factories currently lack a resource-efficient, explainable system that meets Industry 5.0 demands.arxiv | Korota Arsène Coulibaly provider id |
2026-07-24 | 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.