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
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2399497427
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Chapal Barua (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Organizational Efficiency: 1 paper
- Task Completion Time: 1 paper
- Firm 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 reinforcement learning consistently improves project scheduling: pooled evidence from 52 studies shows an 18.7% average makespan reduction and double-digit gains in utilization and throughput, with hybrid DRL models performing best; however, results vary across domains and study settings.openalex | Chapal Barua provider id |
2026-01-01 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.