The Ontario Water Works Consortium: a functional model of source water management and understanding
Bibliographic record
Abstract
With an historical onus on reactive water treatment in North America, most taste and odour (T&O) outbreaks and other water quality issues have been unanticipated and difficult to control. Recent severe outbreaks of these drinking water issues have prompted wider advocacy of a more proactive "source-to-tap" approach, with greater focus on multidisciplinary partnerships among utilities, scientists and management/policy-makers. However, the practical application of this management model is faced with fragmented drainage basins, waterbodies and jurisdictions, and often requires a common issue such as T&O to initiate its development. This paper presents an example of a successful cooperative approach to drinking water management, the Ontario Water Works Research Consortium (OWWRC), consisting of the six major water utilities drawing water from Western Lake Ontario, scientists from the Canadian and Ontario governments and universities, and several other agencies. Established in 1999 following severe T&O outbreaks, the OWWRC has since operated as a highly effective model, employing a science-based approach to T&O management, supporting research on source-water and treatment issues, public outreach and utility surveys. The paper describes this partnership and summarises the results of an OWWRC T&O survey as one of the significant steps towards source-water characterisation undertaken by this cooperative.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".