Microbiological Effectiveness and Cost of Disinfecting Water by Boiling in Semi-urban India
Bibliographic record
Abstract
Despite shortcomings, boiling is the most common means of treating water at home and the benchmark against which emerging point-of-use water treatment approaches are measured. In a 5-month study, we assessed the microbiological effectiveness and cost of the practice among 218 self-reported boilers relying on unprotected water supplies. Boiling was associated with a 99% reduction in geometric mean fecal coliforms (FCs; P < 0.001). Despite high levels of fecal contamination in source water, 59.6% of stored drinking water samples from self-reported boilers met the World Health Organization standard for safe drinking water (0 FC/100 mL), and 5.7% were between 1 and 10 FC/100 mL. Nevertheless, 40.4% of stored drinking water samples were positive for FCs, with 25.1% exceeding 100 FC/100 mL. The estimated monthly fuel cost for boiling was INR 43.8 (US$0.88) for households using liquid petroleum gas and INR 34.7 (US$0.69) for households using wood.
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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 | 0.002 |
| 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 | 0.001 |
| 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".