Influence of pavement surface noise: the Korea Highway Corporation test road
Bibliographic record
Abstract
Because of a significant increase in the number of vehicles using national highway networks that link major urban centers, road traffic noise—with its harmful impact on the environment—has become a major pavement system issue. Therefore, it is necessary to assess the characteristics of different types of pavement and their influence on road traffic noise. The Korea Highway Corporation test road, with eight different pavement surfaces, was used to test and analyze noise from tire–pavement interaction and from vehicle power trains. Noise was measured in a novel test approach using a surface microphone. The results show that traffic noise levels vary widely according to pavement surface type, vehicle type, and vehicle speed. The findings of this investigation can be used to determine appropriate pavement surfaces that will satisfy specific environmental impact assessments for given traffic conditions and requirements.Key words: road traffic noise, tire–pavement noise, power-train noise, surface microphone.
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How this classification was reachedexpand
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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".