Faecal pollution and solar purification of community water sources within Lake Naivasha basin, Kenya
Bibliographic record
Abstract
As in other parts of Africa, and in other developing nations, the rise in the human population and anthropogenic activities within the Lake Naivasha basin is causing an increase in human health risks due to faecal contamination of domestic water sources. This study investigated faecal pollution of community water sources within the Lake Naivasha basin by measuring the densities of total coliforms, Escherichia coli, intestinal enterococci, Clostridium perfringens and heterotrophic bacteria in Lake Naivasha, the Malewa and Gilgil Rivers, and boreholes using membrane filtration techniques and heterotrophic plate count procedures. Selected physico-chemical parameters were also measured in situ from all the water sources sampled. Lakes and rivers had significantly higher microbial abundances than boreholes. Unlike boreholes, surface sources (rivers and lake) showed significant variation with respect to sampling sites for all the microbiological parameters (P < 0.05). The use of solar radiation in water disinfection with temperatures of 75 °C after 30 minutes from pasteurization point (time zero) fully eradicated E. coli and total coliforms from all the water sources. In conclusion, there is faecal pollution in water sources used by communities within the Lake Naivasha basin. The use of solar radiation is therefore recommended for water purification to reduce likely incidences of waterborne diseases.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".