Geomorphometric and Terrain Analysis of the Nigerian Section of the Chad Basin (Bornu Basin) Northeastern Nigeria
Bibliographic record
Abstract
The aim of this paper is to characterize the geomorphology and analyze the terrain of the Bornu basin. This is critical to the understanding of the general landscape configuration, its evolution and morphodynamics in the basin. Mapping was carried using the Shuttle Radar Topographic Mission (SRTM) and Digital Elevation Model (DEM) data of the area. These set of data compliments the data generated from an extensive and detailed mapping of the area adopting the holistic approach geomorphologic field mapping technique. The basins slope generated from DEM of the area indicated five terrain categories. The western end of the basin has the highest slope of 755 to 1582m denoting the margins of the Biu and Jos plateau which slopes down to the lowest slope category of 256m at the shores of Lake Chad. These slope categories exhibits a variety of land forms ranging from rills, gullies, ponds, rivers, deltas, alluvial plains, ridges, hills and sand dunes of variable sizes, shapes and morphological characteristics. The Bama ridge which rises to height of 363m covering a distance of about 340 Km is a major land form in the basin. These land forms and others identified have been formed and are being fashioned by interplay of fluvial and Aeolian processes whose linkages are found to vary both in spatial and temporal dimensions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".