Can bioretention treatment prevent toxicity in aquatic animals exposed to PAH-enriched stormwater runoff?
Bibliographic record
Abstract
Stormwater runoff contains a complex mixture of contaminants including a wide variety of polycyclic aromatic hydrocarbons (PAHs), primarily from tire wear, road wear, and automobile exhaust. Many PAHs are acutely harmful to aquatic animals, resulting in cardiovascular toxicity and even death. To better understand the effects of complex mixtures of dissolved PAHs on aquatic animals, we simulated runoff events on an asphalt surface treated with a PAH-rich top dressing (coal tar based sealcoat) commonly applied in populous regions of the USA and Canada. Runoff was collected during simulated runoff events over three exposure periods separated by one-week intervals of natural weathering conditions. Runoff was both collected untreated and treated by filtering through experimental soil bioretention columns containing 60% sand : 40% compost. Juvenile coho salmon, zebrafish embryos, and the waterflea Ceriodaphnia dubia were exposed to untreated runoff or bioretention treated runoff and monitored for acute lethality and sublethal effects. Both the concentration of PAHs and toxicity to aquatic test animals decreased rapidly as a function of time since sealcoat application. For all exposure trials, bioretention treatment successfully reduced or eliminated lethal and sublethal effects of the PAH-rich sealcoat 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".