Road Salt Impacts Freshwater Zooplankton at Concentrations below Current Water Quality Guidelines
Bibliographic record
Abstract
Widespread use of NaCl for road deicing has caused increased chloride concentrations in lakes near urban centers and areas of high road density. Chloride can be toxic, and water quality guidelines have been created to regulate it and protect aquatic life. However, these guidelines may not adequately protect organisms in low-nutrient, soft water lakes such as those underlain by the Precambrian Shield. We tested this hypothesis by conducting laboratory experiments on six Daphnia species using a soft water culture medium. We also examined temporal changes in cladoceran assemblages in the sediments of two small lakes on the Canadian Shield: one near a highway and the other >3 km from roads where salt is applied in the winter. Our results showed that Daphnia were sensitive to low chloride concentrations with decreased reproduction and increased mortality occurring between 5 and 40 mg Cl – /L. Analysis of cladoceran remains in lake sediments revealed changes in assemblage composition that coincided with the initial application of road salt in this region. In contrast, there were no changes detected in the remote lake. We found that 22.7% of recreational lakes in Ontario have chloride concentrations between 5 and 40 mg/L suggesting that cladoceran zooplankton in these lakes may already be experiencing negative effects of chloride.
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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 | 0.000 | 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".