Coliform bacteria and salt content as drinking water challenges at sand dams in Kenya
Bibliographic record
Abstract
Sand dams can be an effective community-scale solution to increasing water supplies in some arid and semi-arid regions, but there are few studies that have investigated water quality at sand dams. This study investigated the levels of coliform bacteria and salt content as parameters of potential concern. Most water taken from sand dam sources had fecal coliforms present. Median fecal coliforms were in the range of 150-800 cfu/100 ml for unprotected sources (scoop holes, surface water or hand dug wells), levels which are considered high or very high health risk. Pump wells had less contamination, with fecal coliforms detected in one-third of samples in the dry season. Despite this contamination, user surveys indicated that 74% of communities generally view water as clean for drinking, and 72% reported that no or few people in their community treat their water. Salt content in the dry season was in the poor or unacceptable range (above 900 ppm as total dissolved solids) in 33% of water samples. Results suggest that fecal coliforms and salt content represent two types of challenges to water quality at sand dams: fecal coliforms are a health hazard, whereas high salt content potentially reduces the amount of usable water that is available.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".