Uncontrolled Draining of Rainwater and Health Consequences in Yaoundé – Cameroon
Bibliographic record
Abstract
Context: Like many sub Saharan African cities, Yaoundé is experiencing a faster growth of its population and urban perimeter. The urban population has grown from 812 000 inhabitants in 1987 to 2 100 000 inhabitants in 2006. However, this population growth has not been monitored by the city planners and decision makers. Accordingly, the city is lacking basic urban facilities. such as a good sewage system to evacuate urban waste water. Objective: This paper aims at addressing health consequences resulting from inadequate management of rainwater in Yaoundé. Material and methods: From the data gathered by us in the framework of the PERSAN programme focused on urban health, a cross sectional study has been carried out in 2002 and 2006 across the city. Based on socio-environmental and medical surveys, the study covered neighborhoods and 3 034 households in Yaoundé. Results: It comes out that that the present urban draining network is outdated and ineffective. This has led to increasing fl oods in several sectors of the city, with health hazards. It has been noted that many diarrheal diseases in Yaoundé are related to the poor sanitation resulting from urban waste coupled with standing waters. Conclusion: We are of the opinion that to solve this problem, there is urgent need to set up a new town-planning mechanism which takes into account the city’s demographic and space dynamics.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".