Relationship Between Community Complaints and Noise Level During the Construction of a Large Road Infrastructure in Montréal
Bibliographic record
Abstract
While some studies have previously investigated the links between noise annoyance and community complaints related to road and airport transport, the relationship between construction noise and complaints has been little explored. Research conducted by our team during the rehabilitation work of the Turcot interchange in Montreal (Canada) offers a unique opportunity to study community annoyance towards construction noise. The goal of this paper is to explore the associationbetween complaints and noise levelsin the context of the demolition / reconstruction of a large road infrastructure.A total of 1,294 complaints were collected and transcribed between January 6, 2017, and January 11, 2021. Citizens complained mostly about noise, but also about several other aspects related to construction work such as dust and particle pollution. Significant correlations were observed between the number of noise complaints and noise levels, especially the L10 and theConstruction Noise Contribution indicator developed by our research team.Our results show that community noise annoyance, in the form of complaints, is associated with noise levels in the context of the rehabilitation work of a large road infrastructure. However, these correlations are weak, suggesting that other factors may contribute to the genesis of a complaint in an individual. Additionally, temporal aspects (i.e., time between noise exposure and the lodging of a complaint) could explain the weak correlations. To better understand community noise annoyance, we propose, in a future study, to draw adescriptive portrait of these complaints.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".