A review of transport noise management plans in large North American and European cities
Bibliographic record
Abstract
Noise management plans of large cities are diverse along many factors, such as the responsibilities amd interactions at different levels of governance (national vs. local) and definition of noise (measurement-reliant vs. subject-centered). More specifically, for the regulation of transportation noise, there are various ways these sources are identified (by source type or acoustic signal properties), managed (time of day, zoning, and context), and controlled (police v. specialized departments, complaint systems, treatment of private v. public sources). The regulations, official communications and plans for transport noise are analyzed for 20 major North American and European cities (> 500,000 inhabitants) in order to assess current noise management strategies. Over the past 15 years, an extensive body of academic literature has provided grounds for a soundscape approach to urban noise where “appropriate” sounds can be used to positive effect. Current management plans are also examined for existing applications of this soundscape approach. Results will assist in highlighting trends and will feed into the development of best practices for noise management and regulation in large cities. This review is part of a larger project (Sounds in the City), a collaborative research effort with the City of Montreal, to shape the future of urban noise management.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".