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
17Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
1404659979
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Ramona Rupeika-Apoga (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Innovation: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Research Productivity: 1 paper
- Consumer Welfare: 1 paper
- Other: 1 paper
- Regulatory Compliance: 1 paper
- Training Effectiveness: 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 |
|---|---|---|---|
| AI and blockchain are promising tools to curb fraud across alternative finance platforms, yet the field is held back by a dearth of labeled fraud data and inconsistent evaluation practices, limiting robust evidence on real-world effectiveness.openalex | Ramona Rupeika-Apoga provider id |
2026-07-31 | 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.