Escherichia coli contamination of rural well water in Alberta, Canada is associated with soil properties, density of livestock and precipitation
Bibliographic record
Abstract
Waterborne outbreaks of infectious disease continue to be a public health risk, particularly those in areas where testing of private and public small system groundwater systems is left to the owners/overseers of these wells who may not recognize the importance of testing and treatment. Recognizing factors associated with contamination of wells is important for public safety and can encourage well owners/overseers to test regularly and properly maintain drinking water supplies. Tests results for presence/absence of total coliforms and Escherichia coli for private and public untreated well water for the years 2010-2012 (n = 56,609) were provided by the Alberta Provincial Laboratory for Public Health. Tests were geolocated with the Alberta Township Survey System and aggregated to the quarter section. Agricultural independent variables were provided by the Canadian Agricultural Census and monthly cumulative precipitation was calculated using Alberta Agriculture and Forestry’s website of weather station data. Overall frequency of E. coli-positive wells in the study was 1.4%. A marginal multivariable logistic regression model was fit using generalized estimating equations to account for repeat testing of some quarter sections. Three significant factors associated with increased E. coli-positive untreated drinking water wells were identified: soil properties (KSat and sand content), animal density and monthly cumulative precipitation.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".