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
1Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5098489477
ORCID evidence
No valid ORCID is stored.
Observed aliases (4)
- Kefallinos, Paola (openalex, provider refresh)
- Kefallinos, Paola (openalex, source metadata)
- Paola Kefallinos (openalex, provider refresh)
- Paola Kefallinos (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Error Rate: 1 paper
- Other: 1 paper
- Adoption Rate: 1 paper
- Task Allocation: 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 |
|---|---|---|---|
| Public contracting data let ML explain much of the variation in Air Force contract log-prices (XGBoost R² ≈ 0.71), but median dollar-scale errors of about 51% leave forecasts too noisy for autonomous procurement decisions; richer proprietary inputs or NLP-derived technical features could materially improve practical utility.openalex | Kefallinos, Paola provider id |
2026-08-21 | 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.