Noise Reducing Asphalt Pavements: A Canadian Case Study
Bibliographic record
Abstract
Over the last 20 years, many highway jurisdictions have experimented with different asphalts that include blended recycled rubber particles as a way to re-use old tires as well as to monitor the effects of rubber in possibly reducing the aging effects in asphalt pavements. While there have been many claims of noise reduction from different agencies over the years, there was limited conclusive documentation and testing to support the claims. In late 2003, the University of Waterloo's Centre for Pavement and Transportation Technologies (CPATT) and the Regional Municipality of Waterloo embarked on a partnership to first design noise reducing pavement test sections and then secondly to conduct controlled noise testing on four different types of asphalt mixes. The four different surface courses were placed in lengths of 600m. Noise level test results have indicated that the special premium pavement mixes do achieve a reduction in measured noise. The paper will elaborate on the types of materials used, the testing protocol, the measured noise results and the conclusions which will be of use by other municipalities in assessing the merits of using premium surface course asphalts to reduce noise in urban, noise-sensitive environments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".