High rate of drinking water contamination due to poor storage in squatter settlements in Mwanza, Tanzania
Bibliographic record
Abstract
Background: Drinking water of acceptable quality is supposed to be free from faecal coliform and chemical substances that may be hazardous to human health. Water treatment and safe storage at the household level has been advocated as effective means of ensuring safe drinking water. This study was undertaken to determine the microbiological quality of the drinking water at household level in the squatter settlements in the city of Mwanza, Tanzania.Methods: A cross-sectional study was conducted between June 2014 and September 2014. A total of 15 randomly selected water sources (tap) and 207 households’ drinking water samples from these sources were studied to ascertain level of water contamination using Membrane Filtration Method. Pre-tested questionnaire was used to collect demographic and other data regarding water treatment and storage. Data were entered, cleaned and analysed using STATA Version 11.Results: All 15 samples from tap used as water sources were found to be free of indicator organism (Escherichia coli) while 109 (52.66%) of drinking water samples from 207 households were found to be contaminated with E. coli. All contaminated drinking water samples were from containers with no cover and spigot. Conclusions: There is a significant level of deterioration of water quality from the source to the drinking cup. Efforts to ensure quality storage methods for drinking water should be addressed at household level.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".