Assessment of Tire-Additive Transformation Product 6PPD-Quinone in Urban-Impacted Watersheds
Bibliographic record
Abstract
Urban stormwater is a conduit for pollutants such as nutrients and tire-wear compounds through streams to the Great Lakes receiving waters. The transformation product of tire antiozonant 6PPD, 6PPD-quinone (6PPD-Q), is implicated in causing fish mortality. Urban streams entering Lake Ontario and waters adjacent to urban centers in Toronto and Hamilton, Ontario, were assessed for 6PPD-Q and its potential to impact aquatic life. Sampling occurred during fall and spring dry- and wet-weather flows in 2021–2022, overlapping with fish spawning times. Concentrations of 6PPD-Q were elevated during wet-weather (maximum 82 ng/L) in streams with the greatest degree of urbanization and road density but were much lower (2.4–4.5 ng/L) in nearshore waters, suggesting rapid dilution. Precipitation, road density within watersheds, chloride (from road salts), and total phosphorus were correlated with 6PPD-Q concentrations, suggesting that such parameters may provide a means to identify and prioritize key stream habitats for further assessment. A screening level hazard assessment using application factors of 10–1000 and available toxicity end points for fish (rainbow trout, brook trout, coho salmon) showed that 6PPD-Q concentrations may pose a risk in urban streams and nearshore waters of the region. We recommend that stormwater quality control measures be assessed for their effectiveness in reducing 6PPD-Q concentrations.
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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 | 0.001 |
| 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".