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
3Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2420874253
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Vasilios Vasiliadis (semantic scholar, provider refresh)
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
- Decision Quality: 1 paper
- Adoption Rate: 1 paper
- Ai Safety And Ethics: 1 paper
- Innovation Output: 1 paper
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Research Productivity: 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 |
|---|---|---|---|
| Deep learning now powers prediction, personalization and decision intelligence across e-commerce — from recommendations and demand forecasting to pricing and warehouse automation. Yet practical deployment is constrained by scalability, robustness, interpretability and cross-border adaptation challenges that must be solved before these systems can reliably boost platform productivity and trust.openalex | Vasilios Vasiliadis provider id |
2026-02-26 | 5 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 5 cumulative citations. This is a coverage summary, not an author score or h-index.