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
9Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5051858568
ORCID evidence
No valid ORCID is stored.
Observed aliases (4)
- S. L Silva (openalex, provider refresh)
- S. L Silva (openalex, source metadata)
- Sara Pessoa Silva (openalex, provider refresh)
- Sara Pessoa Silva (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Organizational Efficiency: 1 paper
- Ai Safety And Ethics: 1 paper
- Governance And Regulation: 1 paper
- Regulatory Compliance: 1 paper
- Task Completion Time: 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 |
|---|---|---|---|
| A hybrid LLM and legal-knowledge graph deployed in a Brazilian court reportedly speeds processing and improves consistency and explainability, suggesting real-world productivity gains from combining generative models with domain-specific structured knowledge.openalex | Sara Pessoa Silva provider id |
2026-08-19 | 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.