Priorities to inform research on tire particles and their chemical leachates: A collective perspective
Bibliographic record
Abstract
Concerns over the ecological impacts of urban road runoff have increased, partly due to recent research into the harmful impacts of tire particles and their chemical leachates. This study aimed to help the community of researchers, regulators and policy advisers in scoping out the priority areas for further study. To improve our understanding of these issues an interdisciplinary, international network consisting of experts (United Kingdom, Norway, United States, Australia, South Korea, Finland, Austria, China and Canada) was formed. We synthesised the current state of the knowledge and highlighted priority research areas for tire particles (in their different forms) and their leachates. Ten priority research questions with high importance were identified under four themes (environmental presence and detection; chemicals of concern; biotic impacts; mitigation and regulation). The priority research questions include the importance of increasing the understanding of the fate and transport of these contaminants; better alignment of toxicity studies; obtaining the holistic understanding of the impacts; and risks they pose across different ecosystem services. These issues have to be addressed globally for a sustainable solution. We highlight how the establishment of the intergovernmental science-policy panel on chemicals, waste, and pollution prevention could further address these issues on a global level through coordinated knowledge transfer of car tire research and regulation. We hope that the outputs from this research paper will reduce scientific uncertainty in assessing and managing environmental risks from TP and their leachates and aid any potential future policy and regulatory development.
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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 | 0.109 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".