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
1/1OpenAlex citation coverage
Publication dates unavailable. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5125586750
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Akshit Erukulla (openalex, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Consumer Welfare: 1 paper
- Other: 1 paper
- Research Productivity: 1 paper
- Adoption Rate: 1 paper
- Governance And Regulation: 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 |
|---|---|---|---|
| A machine‑learning system is reported to raise retained food after harvest by 3.42% on Indian farms at no extra cost and claims near‑perfect prediction (R²=0.999); however, opaque data provenance and absent out‑of‑sample or field validation make the result fragile and potentially misleading.semantic_scholar | Akshit Erukulla provider id |
Fetched 2026-03-12 | 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.