Comparison of Changes of Road Noise Level Over a Century Quarter: A Case Study of Acoustic Environment in the Mountainous City
Bibliographic record
Abstract
An essential part of a sustainable city is sustainable transport; however, the development of transport has led to the growing noise pollution.It is obvious that the road-traffic noise has negative health impacts on the population in the cities.These effects should be reduced to ensure the sustainability of modern cities.The main purpose of the study was to compare the changes in the noise level in the mountainous city in 2012 and 2016 compared to 1990.A hypothesis was introduced that over the past 26 years, the level and severity of noise during the day and night increased along with traffic and the number of cars.In addition, a comparison of the value of the traffic intensity of passenger cars and trucks during the daytime in the years 2012-2016 was made.Additionally, the noise generated by vehicles during the day and night was compared.On the basis of the results obtained, it can be concluded that the level of noise during the daytime over the last 26 years has clearly decreased.The main factors that reduced the noise level were the improvement of the quality of vehicle fleet, directing transit traffic to the city beltways, as well as the local use of noise barriers.However, the level of noise intensity at night increased significantly.This is due to the increase in the total number of vehicles in the city and their high speed at this time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".