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:
2291844886
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Giovanni Sansavini (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Consumer Welfare: 1 paper
- Fiscal And Macroeconomic: 1 paper
- Regulatory Compliance: 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 safe deep reinforcement learning controller can halve simulated building heating costs while always honoring grid flexibility requests; the adaptive safety filter secures compliance but incurs a small rise in comfort breaches compared with unconstrained RL.arxiv | Giovanni Sansavini provider id |
2026-04-17 | 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.