Application of CCME Procedures for Deriving Site-Specific Water Quality Guidelines for the CCME Water Quality Index
Bibliographic record
Abstract
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 communicating ambient water quality data. Due to the high natural background levels of particular parameters in water bodies throughout the country it is often necessary to use Site-Specific Water Quality Guidelines (SS-WQGs) as opposed to generic national Water Quality Guidelines (WQGs) or provincial Water Quality Objectives (WQOs) in the CCME WQI model to obtain truly representative rankings. SS-WQGs have only been developed for a limited number of ambient water quality sites and this has been a major hurdle to the widespread use and acceptance of the CCME WQI. This paper presents the adaptation and implementation of an existing CCME-approved SS-WQGs derivation method called the background concentration (BC) procedure into a Site-Specific Water Quality Index (SS-WQI) calculator and tool. It discusses the application of the SS-WQI calculator to compute water quality indices for five pristine ambient water quality sites in Newfoundland and Labrador. The effects of using five different BC-based SS-WQGs (mean; median; mean ± one standard deviation; mean ± two standard deviations; 90th and 10th percentile) are examined. The paper also discusses the challenges and benefits of using this methodology and provides recommendations for further testing.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".