Assessing well characteristics as risk factors for bacterial contamination of private wells in Wellington-Dufferin-Guelph, Ontario
Bibliographic record
Abstract
This study aims to identify important well characteristics associated with increased odds of bacterial contamination in the Wellington-Dufferin-Guelph public health unit of Southern Ontario. Identifying risk factors associated with bacterial contamination can aid in the mandate of public health units to promote the safety, and facilitate the testing, of drinking water systems to help minimize the risk of illness. Logistic regression models for adverse bacterial test results based on physical well characteristics were created. Models with the lowest Akaike Information Criterion values were examined for consistently identified characteristics. The odds of bacterial contamination in the Wellington-Dufferin-Guelph region are most associated with the age of the well, the season of testing, having a treatment system on the well, and the presence of potential point contamination sources within 50 feet (15.24 m) of the well. While this information can support the design of targeted public health education campaigns, the current model leaves room for improvement, as the predictive abilities of the models based solely on well characteristic data are limited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".