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.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".