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
5Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
1393230000
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- M. Gysi (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Organizational Efficiency: 1 paper
- Error Rate: 1 paper
- Output Quality: 1 paper
- Task Completion Time: 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 |
|---|---|---|---|
| An AI agent layer paired with a live digital twin automates network change validation, detecting all injected errors and delivering 92–96% diagnostic coverage while cutting validation time to under seven minutes in the authors' tests; results are promising but rely on synthetic scenarios and incidents from a single operator.arxiv | M. Gysi provider id |
2026-04-20 | 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.