Durability and variability of the acoustical performance of rubberized road surfaces
Bibliographic record
Abstract
The use of road surfaces with low noise emission characteristics is one of the actions mostly applied all over the world to decrease the number of road traffic noise annoyed people. Since many Italian roads are going to be paved with such surfaces, the LEOPOLDO project (funded by the Tuscany Region and the Italian Ministry of Transportation) was planned to check the efficacy in time of this action. Among all solutions, rubberized road surface is one of the most applied in USA, Canada, Europe and Asia. This paper describes results obtained by monitoring four rubberized surfaces one year after the laying and by evaluating the time stability of LEOPOLDO one by means of the Close Proximity method (CPX). All surfaces here analyzed are laid in real scenarios, so the actual efficacy of this action is evaluated. The results on the LEOPOLDO surface show spatial homogeneity, a good time stability and a significant noise emission reduction. Instead, analysis of the four rubberized surfaces shows variability in the results, probably due to the pavement installation quality, as supported by the data. Thus, the rubberized road surface looks to be a very efficient mitigation technology, providing the installation have been carried out with care and proficiency.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".