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
2Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5059407088
ORCID evidence
Observed aliases (4)
- AlMahri, Sara (openalex, provider refresh)
- AlMahri, Sara (openalex, source metadata)
- Sara AlMahri (openalex, provider refresh)
- Sara AlMahri (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Innovation: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Task Completion Time: 1 paper
- Firm Revenue: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| An LLM-driven multi-agent system flags and traces deep-tier supply-chain disruptions in minutes for pennies per incident and shows high task accuracy on synthetic tests; but claims rest on limited simulated scenarios and one case study, leaving broader operational impact unproven.openalex | AlMahri, Sara provider id |
2026-01-14 | 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.