Quantitative microbial risk assessment associated with the use of container-based toilets in Haiti
Bibliographic record
Abstract
A container-based toilet (CBT) is a type of ecological toilet that allows users to compost their feces. During emptying, bucket washing, and composting operations, operators are exposed to microbial risks. This paper aims to evaluate these risks using the Quantitative Microbial Risk Assessment (QMRA) method. Nine pathogens prevalent in Haiti were targeted: Ascaris lumbricoides, Campylobacter spp., Cryptosporidium parvum, Escherichia coli O157:H7, Giardia intestinalis, poliovirus, Salmonella spp., Shigella spp., and Vibrio cholerae. Information regarding pathogens' concentration in feces came from scientific literature data. The exposure scenarios considered were those in which operators accidentally ingested a low dose of feces during the aforementioned operations. A Monte Carlo simulation was conducted to address uncertainties. The results showed that the probability of infection is highly elevated, while the probability of illness is generally moderate or minor, except for poliovirus and Ascaris. Preventive measures can be implemented to reduce these risks during various operations, such as wearing gloves, disposable protective masks, and appropriate clothing. It is up to the political authorities to develop guidelines in this regard and to organize awareness-raising activities with the help of local organizations mandated by the relevant authorities to ensure the safer use of technology by households.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".