Environmental characterization of surface runoff from three highway sites in Southern Ontario, Canada: 2. Toxicology
Bibliographic record
Abstract
Highway runoff is a significant source of contaminants entering many freshwater systems. A battery of bioassays was used to assess the degree of runoff toxicity. The toxicity results are described in the spatial and temporal context and are linked to the runoff chemistry. Runoff samples from three sites, representing different classes of highways (high, intermediate and low traffic intensity), were used to assess the degree of runoff toxicity. Runoff from the major multilane divided highway, with the highest traffic intensity (92,000 vehicles/24 h), had the highest levels of contaminants and displayed the greatest toxicity. Variations in toxic responses were observed both seasonally and throughout runoff events. The runoff samples containing high concentrations of road salts from winter maintenance were acutely toxic to Daphnia magna. In general, a sharp decline in runoff toxicity over time showed that the ‘first flush’ was the most toxic. Road solids present in runoff showed moderate to severe toxicity using a nematode bioassay. Consistently, a significant mixed function oxidase (MFO) induction was observed in rainbow trout exposed to runoff with high concentrations of polycyclic aromatic hydrocarbons (PAHs). The data show that vehicular operation, road maintenance and metal highway structures were significant contributors to contaminant-associated toxicity in road runoff.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| 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".