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
0Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5126759111
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Salami Abdul Mohammed (openalex, provider refresh)
- Salami Abdul Mohammed (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Employment: 1 paper
- Adoption Rate: 1 paper
- Firm Productivity: 1 paper
- Hiring: 1 paper
- Organizational Efficiency: 1 paper
- Ai Safety And Ethics: 1 paper
- Research Productivity: 1 paper
- Training Effectiveness: 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 |
|---|---|---|---|
| Machine-learning scheduling promises to reduce hotel labor inefficiency and support retention, but the gains are conditional: without organizational reskilling, data integration and human oversight, deployments are unlikely to deliver promised savings.openalex | Salami Abdul Mohammed provider id |
2026-08-22 | 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.