THE CANADIAN WATER QUALITY INDEX: A TOOL FOR WATER RESOURCES MANAGEMENT
Bibliographic record
Abstract
Since its development in 2001, the Canadian Council of Ministers of the Environment (CCME) Water Quality Index (WQI) has established itself as a valuable tool for water resources management. The CCME WQI model consists of three measures of variance from selected water quality objectives (Scope; Frequency; Amplitude). These three factors combine to produce a value between 0 and 100 that represents the overall water quality. This value is converted into a ranking using an index categorization scheme that is based on expert opinion. Though the CCME WQI was developed for simplifying the reporting of water quality data it has evolved to become a multipurpose tool. This paper presents and discusses diverse examples of the CCME WQI being used as a water resources management tool. Case studies are presented of its use for drinking water quality data communications, ambient water quality data analysis, integrated watershed planning and management in the forestry sector and as a performance measurement tool to assess the effectiveness of best management practices.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.034 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".