What Actions Should Canada Take to Address the Issue of Contaminants of Emerging Concern in Water?
Bibliographic record
Abstract
Meeting the growing demand for access to clean, safe, and reliable water in Canada requires addressing not just traditional water contaminants, but also contaminants of emerging concern (CEC). CECs cause deleterious effects on human health, and yet Canadian drinking water standards currently exclude a majority of them from regulatory control. To ensure long-lasting access to safe drinking water, this paper aims to present policy recommendations for the Canadian legislature including a detailed analysis of the cost implications, feasibility, and ease of implementation of each option using the EHER (environment, health, economy, and reputation) criteria. We recommend a collaborative solution to CECs management which involves academic research funding to comprehensively analyze the risks of CECs and strategies for their removal as well as regulations controlling CECs levels in water streams through reviewed standards and guidelines.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".