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 dates unavailable. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2406627719
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- S. N. B N (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Labor Markets: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Employment: 1 paper
- Firm Productivity: 1 paper
- Firm Revenue: 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 |
|---|---|---|---|
| A Raspberry Pi AI weeder removed 98% of detected weeds in field trials and produced positive hourly profits for small/medium farms (≈₹68.5/hr). However, only about 60% field coverage and limited Wi‑Fi range threaten scalability and wider adoption.semantic_scholar | S. N. B N provider id |
Fetched 2026-03-15 | 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.