Aircraft noise and environmental equity in Montréal: A comparison of noise indicators and an analysis of the impacts of COVID-19
Bibliographic record
Abstract
From an environmental equity perspective, the aim of this paper is twofold. First, we want to verify to what extent vulnerable population groups resided in areas exposed to high levels of aircraft noise before and during the COVID-19 pandemic (2019 and 2020) in the Montréal census metropolitan area. Second, we want to identify whether the use of an aircraft noise indicator rather than another generates significant variations in the results and consequently in terms of affected areas and populations. With the IMPACT web-application, we model aircraft noise contours from three cumulative (Lden, Ldn, Laeq,24h) and a single-event (LAmax) metrics. The model’s input data are retrieved by a website for flight tracking. Next, four variables are extracted from the 2016 Statistics Canada census at a fine scale level (dissemination areas): that is, the percentages of low-income individuals, visible minorities, children under 15 years old, and individuals aged 65 and over. The results show a significant drop in population exposed to aircraft noise in 2020 compared to 2019. In addition, the estimates of populations impacted by aircraft noise differ from one indicator to the next. The logistic regression models indicate that the inequities are inconsistent between cumulative and single-event metrics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".