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Sankore

Named after the University of Sankore in Timbuktu — one of the world’s oldest centres of learning. Every figure here is computed from work-level records in our own graph, never copied from a source’s summary count.

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Coverage is partial and says so. A country appears with its research output only once its works have been swept; until then the portal shows whatever the graph happens to hold for it and labels it as exactly that.

Bayesian Optimized Machine Learning Models for Predicting Gas Flaring Volumes in Nigeria's Oil and Gas Sector

2025NIPES Journal of Science and Technology ResearchJournalOpen access

10.37933/nipes/7.4.2025.si248 Full text0 citations

Authors

6 authors across 2 institutions in 1 country.

Ogbonnia Ogudu EmmanuelFirst author
- Landmark University (NG)
as printed: Ogbonnia O. Emmanuel
Mitong Z. Dorcas
- Federal University of Technology Minna (NG)
Alfa Peter Eleojo
- Landmark University (NG)
Ishola Oluwaseun Joshua
Oghenegueke Fortune Amrevuawho
- Landmark University (NG)
as printed: Oghenegueke F. Amrevuawho
Prisca OlawoyeSenior author
- Landmark University (NG)
as printed: Prisca O. Olawoye

Fields

Assignment is probabilistic — a work belongs to several fields in proportions.

Oil, Gas, and Environmental Issueslevel 3 · 0.97
Natural Resources and Economic Developmentlevel 3 · 0.00
Wind and Air Flow Studieslevel 3 · 0.00

Sources

Several sources describing one work is the point — it means the record rests on more than one authority.

openalexseen September 9, 2026
2025 · NIPES Journal of Science and Technology Research · 0 citations
https://openalex.org/W7125578111

Bayesian Optimized Machine Learning Models for Predicting Gas Flaring Volumes in Nigeria's Oil and Gas Sector · Sankore