Modification and Application of the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) for the Communication of Drinking Water Quality Data in Newfoundland and Labrador
Bibliographic record
Abstract
Abstract In Newfoundland and Labrador (NL), drinking water quality monitoring is conducted by the provincial government on all public water supply systems and results are communicated to communities on a quarterly basis. This paper describes the application of the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) as a communications tool for reporting the drinking water quality results. The CCME WQI simplifies the communication of results while integrating local expert opinion, without challenging the integrity of the data. The NL Department of Environment and Conservation successfully tested the use of the CCME WQI on selected drinking water quality data sets, and developed a phased approach for its implementation as a practical means of presenting available physical, chemical, organic and microbiological results to communities. The CCME WQI index categorization schema was modified by adding a new ranking category to incorporate local expert opinion. This paper describes the development of the phased approach for calculating water quality indices, the testing methodology used, the rationale for modifying the existing CCME WQI index categorization schema, and the implementation of an automated CCME WQI calculator in the provincial drinking water quality database. The paper also discusses the challenges encountered in using the CCME WQI especially with respect to incorporation of contaminants, microbiological and trihalomethanes data. The benefits and downfalls of this application are also discussed.
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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 | 0.032 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".