RELATIONSHIP BETWEEN OPERATIONAL CHARACTERISTICS OF SMALL NON-COMMUNITY DRINKING WATER SYSTEMS AND ADVERSE WATER QUALITY INCIDENTS IN SOUTHERN ONTARIO, CANADA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ensuring that water sources are safe by protecting them from disease causing organisms is integral for the continued health of people as drinking contaminated water leads to waterborne diseases which can be life-threatening. The purpose of this study is to examine small non-community drinking water systems' (SDWSs) operational characteristics and their relationships with adverse water quality incidents (AWQIs) which is defined as presence of total coliforms and/or Escherichia coli. We explored the relationship between operational characteristics of SDWSs and the occurrence of adverse water quality outcomes using de-identified data provided by Wellington-Dufferin-Guelph Public Health, Ontario. We examined the associations between water system operational characteristics and the adverse water quality outcome using logistic regression models. The analyses results indicated that operator training was associated with a lower risk for AWQI. None of the other predictors were significantly associated with AWQI: treatment method, water source, operating period, or sampling frequency. Our research concluded that the presence of operator training, an upstream behavioural determinant, is related to the incidence of AWQIs in SDWSs in Ontario, Canada. The high percentage of SDWSs with no treatment and lack of interest in testing for chemicals are potential areas of concern for ensuring the provision of safe drinking water from these systems.
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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 it