Framework for Environmental Assessment of Tire–Pavement Noise
Bibliographic record
Abstract
Traditionally, the objective of tire–pavement noise studies has been limited to ranking of different pavement surfaces in terms of their potential to generate noise. The potential to generate noise has been assessed by using a variety of sound-level measurements and sound-level measures, typically quite different from those used for the environmental assessment of highway noise. A fundamental methodology is described for the assessment of tire–pavement noise that fits the existing framework for the environmental assessment of highway noise. In this context, it is necessary (a) to express the differences in tire–pavement noise in terms of the sound-level units used for the environmental assessment of highway noise and (b) to consider a realistic situation that includes, for example, sound levels emitted by the entire traffic flow at locations that correspond to outdoor recreational areas of residential dwellings. The process is illustrated by applying it to the environmental assessment of freeway noise in Ontario, Canada, involving two pavement surface types: a dense-graded asphalt concrete surface and a portland cement concrete surface. The results indicate that the difference in sound levels between the two surface types at a residential location adjacent to a freeway can reach up to about 2 or 3 dB(A) L eq (24 h). However, when the typical highway geometry is considered, the presence of noise barriers, and a typical car–truck freeway traffic mix, this difference is reduced to less than 1 dB(A) L eq (24 h). This difference is typically interpreted as having an insignificant environmental impact.
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.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".