Reducing water quality data inequities: A low-cost to a membrane filtration technique for the quantification of Escherichia coli in drinking water in low-resource contexts
Bibliographic record
Abstract
Access to safe drinking water is a recognized human right and a policy priority, reflected in the United Nations’ Sustainable Development Goals (SDGs). To monitor progress on SDG Target 6.1—safely managed drinking water services—many countries now incorporate Escherichia coli water quality testing into nationally representative household surveys, including UNICEF’s Multiple Indicator Cluster Surveys (MICS). The objective of this study was to evaluate multiple aspects of existing MICS water quality testing techniques. A low-cost filtration kit (~$60 compared to ~$1200 for the standard kit) was piloted during a water quality study in Southern Malawi. The low-cost filtration kit performed well with no breakage, leakage or stability issues reported. An existing MICS quality control measure was also assessed. Results support the current practice of using pre-tested locally purchased bottled water to undertake “blank” negative quality control testing. The current practice of having enumerators count E. coli colonies was investigated and was found to be acceptable and valid. To increase the storage capacity of the belt incubation method, a reduced (18- vs. 24-hour) incubation time was investigated. If the purpose is to classify results by risk categories, it would be advisable to incubate samples for the additional 6 hours if after 18 hours a count is observed of only 1 or 2 CFU/100 mL lower than the cut-off for the next highest risk category. Overall, results were encouraging and support the widespread use of the low-cost filtration kit, with potentially significant cost savings. However, we recommend further research to investigate and quantify the impacts of an abbreviated incubation time on water quality results.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".