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:
2380307453
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Mohammad Mushfiqul Haque Mukit (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Innovation: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Other: 1 paper
- Adoption Rate: 1 paper
- Consumer Welfare: 1 paper
- Governance And Regulation: 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 |
|---|---|---|---|
| Machine learning predicts farmer creditworthiness well in a small IMFI dataset (Random Forest R²=0.87; Gradient Boosting F1=0.91), and blockchain is suggested to secure Shariah-compliant records; however, excessive missing data and no out-of-sample testing weaken the case for immediate scale-up.openalex | Mohammad Mushfiqul Haque Mukit provider id |
2026-01-05 | 6 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 6 cumulative citations. This is a coverage summary, not an author score or h-index.