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
4Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2348484024
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- J. Lygeros (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Innovation: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Organizational Efficiency: 1 paper
- Consumer Welfare: 1 paper
- Firm Revenue: 1 paper
- Regulatory Compliance: 1 paper
- Adoption Rate: 1 paper
- Fiscal And Macroeconomic: 1 paper
- Inequality: 1 paper
- Research Productivity: 1 paper
- Skill Acquisition: 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 data-driven, model-free traffic-light controller cuts citywide travel time and CO2 in a large Zurich simulation without bespoke traffic models. The approach substitutes sensor data for costly modeling—speeding deployment and lowering operational costs—though real-world gains hinge on representative data, safety checks and online adaptation.arxiv | J. Lygeros provider id |
2026-03-17 | 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.