Tire–pavement noise and wearing course surface characteristics of experimental Canadian road pavement sections
Bibliographic record
Abstract
An evaluation of the acoustic and surface characteristics of different Canadian pavement types was carried out in 2013 at the test track of the Centre for Pavement and Transportation Technology at the University of Waterloo. Noise testing was performed to determine the coefficient of noise absorption on cored samples and noise emissions in the field using the close proximity and the on-board sound intensity methods. Wearing course characteristics were evaluated with field testing, including visual condition surveys, evaluation of frictional properties with the British Pendulum tester, mean texture depth measurements, and surface profile and roughness evaluation with a walking profiler. As of the time of testing, the noise testing results indicate comparable acoustic properties in both flexible and rigid pavement sections, despite differences in the initial pavement materials, mixes, and surface finishing. With increasing pavement age, the amount of noise emissions increases as the pavement surface is worn down. Comparable friction values are also observed in all pavement sections, in line with the noise testing results. However, this is largely based on the initial construction values. Surface distresses are also not uniformly distributed: they are more severe in the oldest sections and more frequent in the loaded lane, which carries the heaviest traffic loads.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".