Temperature and Aging Effects on Tire/Pavement Noise Generation in Ontarian Road Pavements
Bibliographic record
Abstract
Tire/pavement noise is caused by a complex set of interactions in the contact patch. Managing pavement surfaces and materials has been an effective strategy for noise mitigation, because it is often possible to act at the source of the noise. Since traffic noise is a public concern, due to the effects on heath and the economy of a country, it is crucial to understand the acoustic performance of road pavements through continuous monitoring, because their acoustic properties may diminish over the time. \nA selection of roads in Southern Ontario with several types of pavement and different ages has been identified for this study, including rigid and flexible sections. The survey methodology includes the evaluation of noise at different times of the day to evaluate various temperatures and obtaining, in parallel, the sound pressure and sound intensity levels at the tire/pavement interface using the Close-Proximity (CPX) and the On-Board Sound Intensity (OBSI) methods respectively. Some of the selected road stretches were already been tested in 2008 by the Centre for Pavement and Transportation Technology (CPATT) at the University of Waterloo (UW) and the new results have been compared to the existing ones to determine the aging effects. \nOverall, the results show that sound intensity and sound pressure level raise when the 18 age increases, while temperature performs a minor influence. Also, the results demonstrate 19 that sound intensity and sound pressure levels have a significant variation depending on the 20 type of pavement. Finally, good correlation between CPX and OBSI methods was observed. 21 \n22 \n23
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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".