High-Frequency Data Provides Insight into Chloride Transport Pathways and Exceedances of Chronic Chloride Guidelines for the Protection of Aquatic Life in Streams Impacted by Deicers
Bibliographic record
Abstract
Assessments of elevated stream chloride (Cl) concentrations ([Cl]), predominantly sourced from winter application of road deicers across snow belt regions, are starting to use high-frequency data, more so in the United States (U.S.) than in Canada. Here, [Cl] was derived from high-frequency specific conductance (SC) measurements from nine streams draining urbanized subwatersheds around Hamilton, Ontario, Canada, between May 2020 and April 2021. We assess [Cl] dynamics to understand dominant transport pathways and characterize water quality guideline exceedances to assess ecological risk while comparing Canadian and U.S. methodologies. These streams exhibited an alarming extent of high [Cl] as six streams exceeded the Canadian short-term guideline >90% of both the salting and non-salting seasons. High-frequency stream [Cl] revealed Cl-impacted groundwater maintaining baseflow [Cl], while fast pathways (e.g., sewers) drive [Cl] pulses in the salting season and episodic dilutions in the non-salting season. Application of the higher U.S. guideline gave consistently lower exceedances. Its application of rolling averages to high-frequency data also obscures episodic dilutions that reduce [Cl] below guideline thresholds and may provide brief intervals of refuge to organisms. High-frequency data provided insight into Cl pathways and ecological risk, though exceedance results are sensitive to the guideline methodology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".