Bacterial Contamination on Latrine Surfaces in Community and Household Latrines in Kathmandu, Nepal
Bibliographic record
Abstract
A lack of sanitation infrastructure is a major contributor to the global burden of diarrheal disease, particularly in low-income countries. Access to basic sanitation was identified as part of the 2015 United Nations Sustainable Development Goals. However, current definitions of “basic” sanitation infrastructure exclude community or shared sanitation, due to concerns around safety, equity, and cleanliness. The purpose of this study was to measure and compare bacterial contamination on community and household latrine surfaces in Kathmandu, Nepal. One hundred and nineteen swab samples were collected from two community and five household latrines sites. Community latrine samples were taken before and after daily cleaning, while household samples were collected at midday, to reflect normal conditions. Concentrations of total coliforms and Escherichia coli were measured using membrane filtration methods. Results found almost no differences between bacterial contamination on latrine surfaces in community and household latrines, with the exception of latrine slabs/seats that were more contaminated in the community latrines under dirty conditions. The study also identified surfaces with higher levels of contamination. Findings demonstrated that well-maintained community latrines may be as clean, or cleaner, than household latrines and support the use of community latrines for improving access to sanitation infrastructure in a low-income country setting.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".