Impact of usership on bacterial contamination of public latrine surfaces in Kathmandu, Nepal
Bibliographic record
Abstract
According to the United Nations (UN) Sustainable Development Goals (SDGs), community or public toilets shared by more than one household are not considered “safely managed” under SDG 6.2. However, many populations around the globe, particularly in urban settings, lack access to private sanitation facilities. For this reason, there is a need to evaluate the cleanliness of community or public toilets in these settings and examine best practices for maintaining them. This study had three aims: 1) build on previous data collected in March 2018 at public latrines to determine whether cleaning protocols were sustained, 2) examine relationships between latrine cleanliness and usership, and 3) identify latrine surfaces with higher concentrations of bacterial contamination. In March 2018 and December 2019, swab samples were collected from public latrine surfaces in Kathmandu, Nepal. Sampling occurred in “clean” conditions–after cleaning and before the latrine was opened for use–and “dirty” conditions–during operating hours. Samples were analyzed for concentrations of total coliforms (TC) and Escherichia coli (EC). The number of latrine users prior to the “dirty” sample collection was recorded (in December 2019 only). Results found that both TC and EC concentrations were significantly lower during “clean” rather than “dirty” conditions and both TC and EC concentrations increased with the number of users over time. TC and EC concentrations differed by surface type during dirty and clean conditions (p<0.05). Findings suggest cleaning protocols established at this public toilet site were adequately maintained two years later.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".