Assessment of noise complaints as an indicator of long-term effects of aircraft noise on communities surrounding aerodromes
Bibliographic record
Abstract
Aircraft noise is considered by many as the most burdensome part of aircraft operations. Communities neighbouring airports often express concerns about the possible health effects of chronic exposure to noise. To quantify the long-term effects of environmental noise exposure, researchers often use the metric of annoyance. Annoyance is widely considered to be the most well-corroborated health effect of aircraft noise and a moderating factor for other suspected health effects. Annoyance can also be correlated to average cumulative noise levels, such that higher levels of chronic noise likely evoke higher levels of annoyance within the population. Annoyance data is typically collected using extensive surveys and/or interviews, which are costly and time-consuming. In the absence of annoyance data, complaints are often used as a proxy for annoyance. This research used complaint, noise and annoyance data to demonstrate that complaints do not equate to annoyance, nor do they correlate to cumulative noise metrics. Thus, while complaint data may prove useful in analyzing short-term response to operations, it should not be relied upon for the assessment of long-term impacts from aircraft noise exposure, nor should it be used to direct noise and annoyance mitigation initiatives.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".