Flood risk management in Nigeria: a review of the challenges and opportunities
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
Flooding has become a major hazard in Nigeria in recent years due to a growing population, rapid urbanization and extreme weather events. This study provides a critical review and characterisation flood risk management (FRM) practices in Nigeria with a view to highlighting current weaknesses and opportunities, as well as giving recommendations for practice and for further research. Databases of academic literature, covering a wide range of FRM issues, were systematically queried and mined using suitable keywords. A structured review of the resulting literature was carried out and several past flood events and associated responses reviewed as case studies. Absence of integrated FRM systems, lack of inter agency coordination, substandard and weak infrastructures, inadequate drainage network, high urban poverty, low level literacy, cultural barriers and weak institutions characterize current FRM practices. The study recommends the adoption of an integrated approach to urban infrastructural development starting with a review of ongoing and planned infrastructural systems and projects with a view to optimizing their FRM capabilities while still meeting their intended purposes. The empowerment of more entrepreneurs into FRM solutions development and service delivery as well as the inclusion of FRM concepts and practices into the nation's educational curricula was also recommended. Nigeria also needs a multidisciplinary platform for generating effective strategic policies and efficient operational mechanisms for FRM.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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