Managing uncertainty in the provision of safe drinking water
Bibliographic record
Abstract
The Canadian Water Network, the Alberta Water Research Institute, and the Ontario Centres of Excellence have collaborated to create the Canadian Municipal Water Management Research Consortium, a new initiative to engage municipal water authorities and allow them to access research capacity to tackle mutually identified, critical issues. The challenge of managing uncertainty in the provision of safe drinking water was selected as one such issue. An international expert panel with scientists from Australia, Canada, the USA and Europe was assembled to work with a steering committee of municipal water providers and drinking water regulators. This group has posed the challenge: How best can drinking water providers address risk and uncertainty to assure safe drinking water? Five key drivers to this challenge were identified: the current large list of drinking water contaminants, the inevitable growth of that list as a result of analytical advances not matched by our ability to assess small, mostly immeasurable health risks, the lack of clarity on public expectations for safe drinking water, misunderstanding of new, small risks and a need to assure aesthetic quality. Promoting the means for achieving a common understanding of risk and uncertainty among water providers and regulators was identified as a priority objective. The project has been initiated by developing, in a Canadian drinking water context, working definitions for safe drinking water, risk and uncertainty, with appropriate illustrative examples. The limitations of sole reliance on compliance monitoring for numerical contaminant limits compared with the merits of a preventive risk management/water safety plan approach were elaborated. Based on the foundations adopted, a toolkit is being developed to assist with issues ranging from a risk hierarchy, various products to promote better understanding of how risk assessment is performed, and products to enhance communications with consumers about drinking water safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".