Water Quantity and Quality Dimensions in Public and Environmental Health Among the Maasai of Amboseli Area, Kenya
Bibliographic record
Abstract
A comprehensive water situation analysis is critical in understanding linkages between environmental health, people and livelihoods. This study examined water and public health issues among the Maasai of Kimana near Amboseli National Park. Data was collected on the status, and trends in water quality and quantity, their causes and impacts to the local community using field assessment, interviews with local communities and laboratory analysis on water quality. Results indicated water quantity and quality were declining, and this was attributed to increase in human population, prevalence of irrigated agriculture, and recent climatic changes. The decline was thought to be contamination from human and livestock waste, proximity of homes to water sources, poor sanitation practices, agro – chemicals pollution, high levels of suspended solids and particulate matter. There was a general lack of enforcement by relevant agencies responsible for conservation and use of water resources. Due to communal ownership of resources and lack of resource stewardship, environmental degradation has become prevalent causing soil erosion which contributes to water contamination and sedimentation. A decline in water quantity and quality has led to increased prevalence of waterborne diseases such as dysentery, amoebiosis and typhoid. Therefore the water situation in the Kimana area is leading to negative consequences on the health of local communities. Appropriate intervention strategies are needed to promote sustainable water use and safeguard public health in the area.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".