Turning the Corner on Hazardous Tire Compounds: A Management Framework for Tire Additive Pollution
Bibliographic record
Abstract
Abstract Vehicle tires are complex chemical formulations that abrade during use, releasing tire particles everywhere roadways exist. The recent discovery that the tire additive transformation product 6PPD-quinone (N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine-quinone) was primarily responsible for mortality in sentinel fish species has prompted regulatory and scientific scrutiny of tire additives as contaminants subject to widespread human and ecological exposure. Tire additives pose a global pollution challenge to human and ecosystem health due to their high emissions via tire wear particles combined with loss from in-use and waste tire materials. Such releases often occur in close proximity to humans, and mobilized material or chemicals are easily transported to habitats where adverse effects are possible. This issue demands a commensurate policy response that remains unaddressed by existing pollution management policies. We here propose five principles for managing tire additives: mandating nonhazardous alternatives and their transformation products, acknowledging impacts throughout tire life cycles, transparency in tire compositions, characterizing effects, and international harmonization. Following these principles, we outline a Management Framework for Tire Additive Pollution (MF-TAP) that recommends a phased regulatory approach, data transparency, independent expert panels, and internationally coordinated governance to drive the development and use of alternative, nonhazardous tire additive compounds. Managing tire additives according to the MF-TAP will allow us to better address the pollution potential of hazardous tire additives and reduce their impacts on human health and ecosystems.
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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 | 0.055 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.011 | 0.020 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".