Where's the pump? Associating sporadic enteric disease with drinking water using a geographic information system, in British Columbia, Canada, 1996–2005
Bibliographic record
Abstract
We investigated whether risk of sporadic enteric disease differs by drinking water source and type using surveillance data and a geographic information system. We performed a cross-sectional analysis, at the individual level, that compared reported cases of enteric disease with drinking water source (surface or ground water) and type (municipal or private). We mapped 814 cases of campylobacteriosis, cryptosporidiosis, giardiasis, salmonellosis and verotoxigenic Escherichia coli infection, in a region of British Columbia, Canada, from 1996 to 2005, and determined the water source and type for each case's residence. Over the 10-year period, the risk of disease was 5.2 times higher for individuals living on land parcels serviced by private wells and 2.3 times higher for individuals living on land parcels serviced by the municipal surface/ground water mixed system, than the municipal ground water system. Rates of sporadic enteric disease potentially differ by drinking water source and type. Geographic information system technology and surveillance data are accessible to local public health authorities and used together are an efficient and affordable way to assess the role of drinking water in sporadic enteric disease.
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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 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".