Optimal site selection for nuclear power plants in Nigeria using geospatial multi-criteria-evaluation techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To ensure safety, environmental sustainability, and operational efficiency, nuclear power plants must be meticulously planned and evaluated before they can be constructed. This study aims to determine whether nuclear power plants are suitable for construction in Nigeria based on a geospatial Multi-Criteria Evaluation (MCE) approach within a Geographic Information System (GIS). Nuclear power presents a practical alternative because of its significant efficiency and minimal greenhouse gas emissions. To determine the most viable locations for nuclear power plants, the research combines a range of spatial datasets, including Digital Elevation Models (DEM), population density maps, drainage networks, transportation networks, and geological fault maps. A spatial data processing method is employed using ArcGIS 10.4.1, which includes; map overlay operations, buffer analysis, geoprocessing, and map algebra. The criteria evaluated in the study include; relief areas with elevations above 700m to avoid flooding, lower population density areas to minimize risks exposure, areas with 20km proximity to water bodies for cooling nuclear reactors, and 20km minimum distance from fault zones for seismic stability and safety. Results based on the identified criteria indicates several states (13)- Kaduna, Katsina, Plateau, Gombe, Borno, Adamawa, Taraba, Benue, Cross River, Zamfara, Ondo, Kano, Nassarawa, and the Federal Capital Territory (FCT) - exhibit optimal conditions for the selection of nuclear power plant sites. The findings of this study are consistent with those of countries such as France, South Africa, and Canada, which use spatial evaluation techniques for site selection similar to those used in this study. Providing insights into the optimal site selection could contribute to energy security and Sustainable energy development in Nigeria
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it