Evaluation of the Relationships between Urban Infrastructure and Flood Disaster in Gombe Metropolis
Bibliographic record
Abstract
The purpose of this study is to evaluate the relationship between urban infrastructure and flood disaster in Gombe metropolis, Nigeria. In this research work, the survey research design was used to obtain data from the field. Stratified sampling technique was adopted in determining the study sample which consisted of 250 households (i.e. 13% of the population, made up of 1,923 households). 250 questionnaires were administered on household heads in the study area; however, only 237 questionnaires were retrieved and used for analysis. Spearman’s rank correlation and multiple linear regression analysis were used to examine the relationships between the criterion variable (flood disaster) and the predictor variables (urban infrastructure). The study showed that inadequacy of appropriate urban infrastructure is the major factor responsible for flood disaster in Gombe metropolis [R = 0.792, P = 0.000<0.01(2-tailed)]. It was recommended among others, that there should be improvement on the maintenance of available drainage infrastructure in the metropolis and the integration of solid waste management to prevent over flowing of flood as a result of blockage of drains. Government and all other stakeholders should expedite the provision of appropriate urban infrastructure in the metropolis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".