Neighborhood diversity of potentially pathogenic bacteria in drinking water from the city of Maroua, Cameroon
Bibliographic record
Abstract
This study examined the spatial variation of potential gastrointestinal pathogens within drinking water sources and home storage containers in four neighborhoods in Maroua, Cameroon. Samples were collected from source (n = 28) and home containers (n = 60) in each study neighborhood. Pathogen contamination was assessed using quantitative polymerase chain reaction, targeting Campylobacter spp., Shiga toxin producing Escherichia coli (virulence genes, stx1 and stx2), and Salmonella spp. Microbial source tracking (MST) targeted three different host-specific markers: HF183 (human), Rum2Bac (ruminant) and GFD (poultry) to identify contamination sources. Staphylococcus aureus and the tetracycline-resistance gene (tetQ) were assessed to measure human hand contact and presence of antibiotic-resistant bacteria. Pathogen/MST levels were compared statistically and spatially, and neighborhood variation was compared with previously collected demographic information. All the test fecal markers and pathogens (except Arcobacter) were detected in home and source samples. Two neighborhoods tested positive for most pathogens/MST while the others only tested positive for one or two. Spatial variation of pathogens/MST existed between sources, storage containers, and neighborhoods. Differing population density and ethno-economic characteristics could potentially explain variation. Future research should explore the influence of demographic and ethno-economic factors on water quality during microbial risk assessments in urban Africa.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".