High-frequency observations reveal acute chloride pulses and chloride legacy effects in an urbanizing watershed impacted by road salting
Bibliographic record
Abstract
In watersheds impacted by urban growth and road salt usage, increasing stream chloride (Cl-) concentrations are well-documented. Peaks in stream Cl- concentrations that exceed chronic and/or acute water quality guidelines are typical in the winter salting season when Cl- (from Cl--based de-icers) is flushed from the landscape but are not easily measured with grab samples. In some cases, chronic Cl- conditions persist into the summer growing season due to a build-up of Cl- in the subsurface. Estimating the proportion of Cl- loads transported in the salting and non-salting seasons is of interest for tracking the relative role of subsurface Cl- pools to the annual load, as well as the influence of runoff events on loads across the two periods. In this study, we made use of a 6-year record of high-frequency stream Cl- concentrations from an urbanizing watershed in southern Ontario, Canada. High-frequency measurements revealed that the acute and chronic water quality guidelines for Cl- were exceeded for 7 and 97 % of the study period, respectively. Salting season Cl- loads were 2 to 5 times higher than in the non-salting season, but surprisingly, inter-event periods contributed 21 to 56 % of the annual load across years. The results of this study illustrate the utility of high-frequency sensors for identifying water quality extremes that negatively impact aquatic ecosystems, identifying Cl- transport pathways, and tracking the build-up of legacy Cl- in the subsurface.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".