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
9Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2052511036
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Srinivasan Iyengar (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Task Completion Time: 1 paper
- Adoption Rate: 1 paper
- Other: 1 paper
- Task Allocation: 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 |
|---|---|---|---|
| Siting modular AI compute at wind farms could unlock vast, underutilized renewable capacity—890+ GW lie within 50 ms of Azure data centers—and a new router, XWind, cuts P99 inference latency by up to 52% in a 64‑GPU emulation, making behind‑the‑meter AI deployments materially more performant. But the results are from a controlled testbed and feasibility mapping, leaving open questions about costs, grid impacts, and real‑world scalability.arxiv | Srinivasan Iyengar provider id |
2026-05-22 | 0 |
Citation observation summary
Semantic Scholar 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.