Semi-quantitative estimation of Escherichia coli levels in public drinking water sources in northern Haiti
Bibliographic record
Abstract
Prior research in Milot, Haiti, documented that public water sources are commonly fecally contaminated, as indicated by the presence of Escherichia coli. However, the degree of contamination was not assessed. In this study, the degree of fecal contamination in public drinking water sources was determined. Further, the usefulness of sanitary inspection surveys to predict fecal contamination was evaluated. A convenience sample of public water sources was tested using a semi-quantitative assay, which estimates the most probable number (MPN) of E. coli/100 mL of water. Each source was evaluated using the World Health Organization sanitary inspection score and classified as improved or unimproved. Sixty-three water sources were tested, of which 27 (43%) had <1 MPN/100 mL, 19 (30%) were contaminated from 1 to 100 MPN/100 mL, and 17 (27%) were contaminated with >100 MPN/100 mL. Some improved water sources were contaminated with >100 MPN/100 mL. The sanitary inspection score did not distinguish between sources that were and were not contaminated with E. coli. In Milot, Haiti, public water sources can be highly contaminated with E. coli. Since neither the categorization of a water source as improved or unimproved nor the sanitary inspection score can predict contamination, routine microbiological testing is justified.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".