Assessing Community Adaptation Strategies to Floods in Flood-Prone Areas of Urban District, Zanzibar, Tanzania
Bibliographic record
Abstract
Floods disasters around the world have increased for the last 20 years and affected billions of people. The same has been observed in Zanzibar, which resulted in severe impacts in many parts of the urban-west region and affected many people, threaten several lives and caused substantial economic losses. Therefore, this study intended to assess the community adaptation strategies to floods, the genesis of those strategies and the limiting factors for each adaptation strategies in flood-prone areas in the Urban District in Zanzibar, Tanzania. It involved 399 households. Data were collected using an interviewer-administered questionnaire for heads of the households to assess their adaptation strategies. The study discovered that the community has been employing different adaptation strategies to reduce the floods risk at pre, during and after floods. Before flooding is cemented the floor, while during flooding moved to another place and after flooding did the structural repairs of their houses and recommendations to the government on providing necessary support are delineated.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".