Salt-Laden Winter Runoff and Freshwater Mussels; Assessing the Effect on Early Life Stages in the Laboratory and Wild Mussel Populations in Receiving Waters
Bibliographic record
Abstract
Abstract The widespread use of road salt for winter road maintenance has led to an increase in the salinity of surface water in many seasonally cold areas. Freshwater mussels have a heightened sensitivity to salt, which is a concern, because many Canadian mussel species at risk have ranges limited to southern Ontario, Canada’s most road-dense region. This study examined the effect of winter road runoff on freshwater mussels. The impact of two bridges that span mussel habitat in the Thames River watershed (Ontario, Canada), the second most species-rich watershed for mussels in Canada, were studied. During a winter melt event, bridge runoff, as well as creek surface water surrounding the bridges were collected. Chloride concentrations in samples from bridge deck and tile drains varied (99–8250 mg/L). In general, survival of Lampsilis fasciola glochidia exposed to those samples reflected chloride levels (e.g. 84% at 99 mg/L; 0% at 8250 mg/L), although potassium (60 mg/L) may have at least contributed to toxicity in one sample. Serial dilution exposures with the two most toxic runoff samples revealed 48-h glochidia EC50s of 44% (McGregor Creek Tile Drain) and 26% (Baptiste Creek Deck Drain). During the melt event, the chloride concentrations in creek surface waters downstream of the bridges ranged from 69 to 179 mg Cl − /L; effects on glochidia (viability 77–91%) exposed to those waters was minimal. There were no live mussels surrounding one bridge (Baptiste Creek), likely due to poor habitat. At the other targeted bridge (McGregor Creek), fewer mussels were found close (< 100 m up- or downstream) to the bridge than further (> 200 m) away. However, other contributing factors, including agriculture, were present at both study areas.
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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 | 0.000 | 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".