Non-acoustic factors and their role in aircraft noise annoyance
Bibliographic record
Abstract
Non-acoustic factors have been acknowledged for some time as likely contributors to aircraft noise annoyance. They help explain why a given level of noise exposure can evoke severe annoyance in one person but not in another. Multiple analyses have concluded that non-acoustic factors explain more variance in annoyance results than noise exposure levels do. That begs questions as to why noise exposure levels are currently the only prescribed predictor of annoyance, and why regulating agencies continue to focus on only reducing noise exposure to combat annoyance. The subjective nature and lack of thorough understanding of non-acoustic factors has rendered them unusable for regulatory purposes, or even as topics of discussion with various stakeholders. What then is the purpose to study non-acoustic contributors to noise annoyance, other than to dismiss severe annoyance by implicating personal, attitudinal, or situational factors rather than the noise itself? This discussion suggests mechanisms by which non-acoustic factors contribute to annoyance and proposes practical ways to incorporate this knowledge in the prediction and mitigation of annoyance.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".