Evaluating the potability of domestic water supply sources using water quality index in Kilombero district, Tanzania
Bibliographic record
Abstract
ABSTRACT The decline in water quality in various bodies of water has led to significant health risks for individuals relying on these sources for drinking and domestic use. The prevalence of waterborne diseases in some areas is attributed by inadequate water quality assessments of supply sources. This study focused on evaluating the potability of domestic water sources in the Kilombero district of Tanzania, where approximately 70% of the population depends on untreated natural sources. Two models were utilized: the Weighted Arithmetic Water Quality Index (WAWQI) and the Canadian Council of Ministers of Environment Water Quality Index (CCME WQI), analysing 15 water quality parameters. The WAWQI identified three groundwater sources as poor quality for consumption, while indicating seasonal improvements in water quality – from 12 in the wet season to 13 in the dry season. Conversely, the CCME WQI classified all 15 sources as potable, with an increase in ‘Excellent’ ratings from 9 to 13 between seasons. The findings showed that all surface water sources were deemed potable, while 67% of groundwater sources met quality standards. The remaining 33% of boreholes were categorized as having poor quality. Overall, both models indicated better water quality during the dry season, with surface water generally exhibiting higher quality than groundwater.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".