What pandemic era travel restrictions revealed about aircraft noise, complaints and annoyance—SONICC 2020
Bibliographic record
Abstract
At the start of COVID-19 travel restrictions, Toronto Pearson International Airport experienced an approximate 80% reduction in traffic. This gave an unprecedented opportunity to investigate the impacts that a drastic reduction in aircraft noise would have on the communities surrounding the airport. Using the results of the Survey of Noise Impacts on Canadian Communities (SONICC) distributed in the summer of 2020, this research evaluated pre-pandemic and amidst pandemic aircraft noise annoyance in neighbourhoods surrounding Pearson Airport. The research investigated the effects of air traffic reduction on noise levels, complaint behaviour and annoyance. Complaint volumes correlated closely to the number of operations, experiencing a significant reduction. Despite the notable reduction in complaints, many complainants continued to vigorously complain, and some locations even experienced an increase in complaints. Pre-pandemic compared to amidst pandemic annoyance experienced reductions proportional to the average reduction in noise exposure. Despite significant reductions in noise, 33% of pre-pandemic highly annoyed (HA) respondents, remained highly annoyed, suggesting that anything short of a complete halt of operations would result in severe annoyance amongst a portion of the population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".