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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.015 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it