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
3Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2259029547
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Raquel Soriano-Gonzalez (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
Claim outcomes
- Other: 1 paper
- Output Quality: 1 paper
- Firm Revenue: 1 paper
- Adoption Rate: 1 paper
- Decision Quality: 1 paper
- Organizational Efficiency: 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 |
|---|---|---|---|
| Profit-aware classifiers tailored to operational limits improve detection of high-risk cold-start auto-insurance customers and raise expected portfolio profit relative to a standard baseline (p < 0.001). A balanced bagging ensemble gives the best practical trade-off for regulated deployment, while a lightweight Transformer delivers stronger robustness and generalization under data perturbations.openalex | Raquel Soriano-Gonzalez provider id |
2026-04-17 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.