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
4Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2088436241
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- M. Huz (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Governance And Regulation: 1 paper
- Market Structure: 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 |
|---|---|---|---|
| Simple models beat AI nationally, but machine learning shines where markets are complex: linear regression predicts agricultural credit volatility best overall in Ukraine (2015–2020), yet ANN and gradient boosting improve accuracy by up to 10.6 percentage points in shock-prone regions, supporting a 'precision banking' hybrid approach.openalex | M. Huz provider id |
2026-01-27 | 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.