Risk assessment of down-the-drain chemicals at large spatial scales: Model development and application to contaminants originating from urban areas in the Saint Lawrence River Basin
Bibliographic record
Abstract
Chemicals released into freshwater systems threaten ecological functioning and may put aquatic life and the health of humans at risk. We developed a new contaminant fate model (CFM) that follows simple, well-established methodologies and is unique in its cross-border, seamless hydrological and geospatial framework, including lake routing, a critical component in northern environments. We validated the model using the pharmaceutical Carbamazepine and predicted eco-toxicological risk for 15 pharmaceuticals in the Saint-Lawrence River Basin, Canada. The results indicated negligible to low environmental risk for the majority of tested chemicals, while two pharmaceuticals showed elevated risk in up to 13% of rivers affected by municipal effluents. As an integrated model, our CFM is designed for application at very large scales with the primary goal of detecting high risk zones. In regulatory frameworks, it can help screen existing or new chemicals entering the market regarding their potential impact on human and environmental health. Due to its high geospatial resolution, our CFM can also facilitate the prioritization of actions, such as identifying regions where reducing contamination sources or upgrading treatment plants is most pertinent to achieve targeted pollutant removal or to protect drinking water resources.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".