Bibliographic record
Abstract
Tire Road and Wear Particles are a major source of microplastic emissions. Tire and Road Wear Particles are important to study and understand as there are alarming amounts found in various environments. Currently, Tire and Road Wear Particles compared to other microplastics are not studied as rigorously in literature but are becoming a larger field of study due to their impact on emissions control. Tire Road and Wear Particles are commonly found as Styrene Butadiene Rubber Butadiene Rubber, and Natural Rubber To understand and quantify tire wear, experimental and mathematical models are developed to estimate tire wear. Tire wear can be measured experimentally using semi-empirical models, predetermined data, and sensor technologies. Tire wear is also measured mathematically using different modeling approaches and different friction models. This review discusses different and popular methodologies for estimating tire wear through experimental and simulated environments. Furthermore, discusses a review of the literature regarding tire wear emissions and its impact on the environment. Finally, it is evident that an accurate simulated tire wear model can be developed in the future alongside a driver model to predict tire wear emissions. Received: 6 July 2023 | Revised: 9 August 2023 | Accepted: 10 August 2023 Conflicts of Interest The authors declare that they have no conflicts of interest in this work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".