Municipal drinking water source protection in low income countries: Case of Buea municipality- Cameroon
Bibliographic record
Abstract
Source water contamination poses a risk to public health and increases the cost of drinking water treatment.Source water protection is a proactive approach for the enhancement of drinking water quality and quantity.A combination of research methods (desk review, interviews and reconnaissance field appraisal) have been used to identify major drinking water sources in Buea, land use activities which constitute potential threats and pathways of contamination of these sources as well as the capacity for source water protection in Buea.Our findings revealed that anthropogenic activities around the six major drinking water sources studied present visible potential threats and pathways for contamination, and that source water protection has not been given adequate attention in the planning and development of Buea.The potential human and social capacities are limited by the lack of financial and technical resources.This is further compounded by the fact that institutional arrangement does not facilitate the integration of land and water management at the local level.A framework for local strategic multi-stakeholder source water protection with the potential to foster leadership, pull together available resources from different stakeholders and reduce potential resistance to the integration of land and water management has been proposed.There is an urgent need for the adoption of a precautionary approach and research to develop baseline data that will enable comprehensive source water protection measures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".