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
5Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5129358573
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- A. Dinesh Kumar (openalex, provider refresh)
- A. Dinesh Kumar (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Automation Exposure: 1 paper
- Decision Quality: 1 paper
- Firm Productivity: 1 paper
- Organizational Efficiency: 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 |
|---|---|---|---|
| Probabilistic AI methods substantially sharpen prediction of supply‑chain disruptions—simulations show 20–60% chances of high‑risk events and regression models explain 78% of disruption variability—suggesting firms adopting AI risk analytics can materially reduce economic losses.openalex | A. Dinesh Kumar provider id |
2026-09-15 | 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.