Durability and variability of the acoustical performance of rubberized road surfaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it