Roadside snowmelt: a management target to reduce lake and river contamination
Bibliographic record
Abstract
Major ion concentrations have greatly increased in many north temperate lakes and rivers over the last 3 decades as the result of deicing materials applied to their surrounding roads in winter. Salt-based deicing will likely continue or increase in the future, and preventative management strategies require an improved understanding of the flow pathways and timing of road salt and associated contaminant fluxes to downstream receiving waters. In the present study, we focused on the catchment of Saint-Charles River and its reservoir that provides drinking water for Quebec City, Canada. Major ion concentrations were measured in river waters and snowbanks along roads in subcatchments that differed in degree of urbanization, and during the same winter–spring period a mooring system was installed in the reservoir to continuously record conductivity. Large significant differences were found in the concentration and temporal behavior of ions between urbanized and forested watersheds. Snow sampled at 1 m from the roads had elevated concentrations of salts and other contaminants, and the highest solute concentrations in the reservoir and river waters occurred during snowmelt events. The results indicate that management of roadside snowmelt runoff during thawing events may largely prevent salt-associated contamination, and that winter snowmelt will require increasing attention as the climate continues to warm.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".