Empirical Model Valuation of Urban Agriculture Vulnerability to Flooding in Makurdi, Benue State, Nigeria
Bibliographic record
Abstract
For this study, geospatial technology was used to assess agricultural lands vulnerable to flooding in Makurdi, Benue State, Nigeria. Six thematic layers of factors influencing flood occurrences in the study area were generated from monthly rainfall, land use/cover, drainage density, soil, digital elevation model and slope. Pairwise comparison of the Analytical Hierarchy Process was used to derive the weights for each factor using expert’s judgements and literature. Weighted overlay model from the spatial analysis tool in the ArcGIS 10.4 environment was used to perform the vulnerability modelling. Expert’s judgement on the relative factors influencing flood in the study area was: rainfall (25%), elevation (22%), slope (20%), drainage density (13%), soil type (8%) and land use/cover (12%). The consistency ratio of the analysis was reasonable: (CR= 0.078). Results from the model demonstrated land vulnerability to urban agricultural flooding in the study area ranging from areas of very highly vulnerable to very low vulnerable areas, with farmlands along the floodplains of River Benue falls within the very highly vulnerable areas. The elements at Risk are; Farmland 537.6 (66.1%), Irrigation Land 40.5 (5.0%) and Built-up Land 125.8 (15.5%).
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".