Distribution methodology for aircraft noise annoyance surveys
Bibliographic record
Abstract
Annoyance is one of the most common effects of aircraft noise on individuals. The prevalence of severe annoyance within a community is a metric that informs regulatory noise exposure thresholds and guidelines. It is therefore critical that accurate annoyance data is collected through community surveys, which are typically distributed to areas affected by various levels of aircraft noise, as defined by average-day type noise exposure contours. This distribution methodology excludes segments of the population that are affected by noise but underrepresented by these types of contours. Here are presented the results of two community surveys executed around Toronto Pearson International Airport, using different distribution methodologies. The first survey identified five zones for distribution based on noise exposure contours. The second survey was distributed within a 750-meter radius around 25 noise monitoring terminals in the vicinity of the airport. The two surveys yielded different annoyance results, particularly as they relate to the locations of highly annoyed respondents. A prevalence of severe annoyance was observed in areas that were intermittently affected by aircraft noise and thus out of the range of average-day type noise contours. It was concluded that a more comprehensive approach for survey distribution is necessary to ensure unbiased annoyance results.
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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 | 0.047 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".