Flood Risk Assessment of Residential Neighbourhoods in Calabar Metropolis, Cross River State, Nigeria
Bibliographic record
Abstract
The study is aimed at examining the vulnerability of some residential neighbourhoods in Calabar to the menace of flooding with a view to determining residential areas of high, medium and low flood risk. Two hypotheses were formulated such as: there is no significant relationship between the magnitude of flood, and the vulnerability of residential neighbourhoods and the elements-at-risk to flood in residential neighbourhoods in Calabar do not vary significantly according to the topography of the area. The major primary data were obtained from the metric measurement of the coverage of flood and the assessment of the numerical value of the residential buildings considered vulnerable to flood within the areas measured. Secondary data were also obtained from the collection of both published and unpublished materials and data on flooded buildings and displaced persons were also obtained from the State Emergency Management Agency (SEMA), Calabar. The data were analyzed using descriptive statistics and hypotheses tested using the regression coefficient of the least square method and scatter grams for prediction. The results of the hypotheses were found to be significant as the magnitude of flood determined the vulnerability of some residential neighbourhoods. Vulnerability was found to be higher in low lying residential neighbourhoods. The study, however, recommends among others, planned and autonomous adaptation responses, flood plain zoning to urban agriculture, landscaping and recreational uses. Proper channelization of Calabar urban drainage system, stringent flood control legislation, and development control measures should be enforced so as to discourage people from building on or near flood-prone areas of Calabar.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".