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:
2447941055
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Ezeokechukwu Chiemere Victor (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Governance And Regulation: 1 paper
- Adoption Rate: 1 paper
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Task Completion Time: 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 |
|---|---|---|---|
| An explainable AI layer makes Earned Value Management proactive: when applied to 1,847 federal IT investments, XGBoost+SHAP cut average risk-detection lag from 2.3 to 0.7 reporting cycles and raised at-risk project recall from 61% to 89%, with program managers rating the AI explanations as substantially more actionable.openalex | Ezeokechukwu Chiemere Victor provider id |
2026-01-01 | 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.