Quieted City Sounds during the COVID-19 Pandemic in Montreal
Bibliographic record
Abstract
This paper investigates the transformation of urban sound environments during the COVID-19 pandemic in Montreal, Canada. We report on comparisons of sound environments in three sites, before, during, and after the lockdown. The project is conducted in collaboration with the Montreal festival district (Quartier des Spectacles) as part of the Sounds in the City partnership. The analyses rely on continuous acoustic monitoring of three sites. The comparisons are presented in terms of (1) energetic acoustic indicators over different periods of time (Lden, Ld, Le, Ln), (2) statistical acoustic indicators (L10, L90), and (3) hourly, daily, and weekly profiles of sound levels throughout the day. Preliminary analyses reveal sound level reductions on the order of 6–7 dB(A) during lockdown, with differences more or less marked across sites and times of the day. After lockdown, sound levels gradually increased following an incremental relaxation of confinement. Within four weeks, sound levels measurements nearly reached the pre-COVID-19 levels despite a reduced number of pedestrian activities. Long-term measurements suggest a ‘new normal’ that is not quite as loud without festival activities, but that is also not characterizable as quiet. The study supports reframing debates about noise control and noise management of festival areas to also consider the sounds of such areas when festival sounds are not present.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".