Managing the noise environment in the context of densification: Issues, expertise, methods, and limits
Bibliographic record
Abstract
Urban densification is often proposed as a solution to ecological problems. Various densification models are studied in the literature, but noise aspects are often neglected. Based on a case study in Quebec City, environmental noise assessment methods (manual and automatic surveys, simulation, etc.) have been explored to identify the skills, expertise, and data needed to integrate the noise dimension into densification projects, and how to choose a model suited to the urban planning context. The availability and quality of data will also be assessed to identify any gaps. Using a combination of morphological analysis and sound simulations, the results will provide a better understanding of the influence of sound quality on densification choices. A discussion about the implications for sustainable urban planning and residents' quality of life, as well as the criteria to be considered in densification strategies to integrate the noise environment effectively. This project will provide answers on the impact of densification models on the noise environment and urban quality of life and allow the development of a framework for improved urban practices.
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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".