Drinking-water management in Canadian provinces and territories: a review and comparison of management approaches for ensuring safe drinking water
Bibliographic record
Abstract
Abstract Drinking-water management systems (DWMSs) represent the primary means for preventative management of a drinking-water supply and are defined as a system of policies, procedures and administrative/behavioral controls designed to ensure safe drinking water from source to tap. With influence and inspiration ranging from safe food handling to industrial quality management, DWMSs can take, and have taken, many different forms throughout the world. This variability is especially true in Canada, a country with a decentralized governance structure, where provincial and territorial governments are mostly autonomous in regard to drinking-water governance and management. While this has resulted in comprehensive DWMSs in provinces such as Ontario, less-proactive provinces and territories have fallen behind and may be exposing consumers to under-protected and vulnerable drinking-water supplies. This paper includes a review and comparison of the existing Canadian national, provincial and territorial approaches to drinking-water management, the World Health Organization Water Safety Plan Recommendations, national DWMSs from Australia and New Zealand, and also includes widely applied, generic quality management systems. This information is then used to gauge the comprehensiveness of DWMSs in Canada and highlight potential management gaps and policy recommendations for the development of new, or improving existing, DWMSs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".