The Association of Acoustic and Non-Acoustic Factors with Severe Aircraft Noise Annoyance - Results of the Survey of Noise Impacts on Canadian Communities
Bibliographic record
Abstract
Many Canadians are affected to various extends by environmental noise. Those living near airports and flight paths are exposed to aircraft noise that can cause severe disturbance and annoyance amongst the population. Annoyance is the most common effect of aircraft noise exposure, and as such, is a key metric in regulations and guidelines. However, it is anecdotally understood that annoyance from aircraft noise cannot be attributed to a measured noise level alone and that there are other contributing factors. Thorough understanding of noise annoyance and all possible acoustic and non-acoustic contributors is critical to its management. The Survey of Noise Impacts on Canadian Communities 2021 (SONICC 2021) was a questionnaire distributed around Toronto Pearson International Airport, which sought to identify both acoustic and non-acoustic factors associated with severe noise annoyance. While the analysis in this paper noted that prevalence of severe annoyance increased with higher noise exposure, noise levels alone were not the best predictor of a respondent’s likelihood of being highly annoyed. Consideration of situational, personal, and attitudinal factors such as perceived change in noise, habituation, feeling of unfairness, and noise sensitivity significantly improved the ability to predict severe annoyance. This paper shares the results of SONICC 2021 and suggests how these findings can inform a more holistic approach to annoyance prediction and mitigation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".