Using appropriate aircraft noise metrics for various applications
Bibliographic record
Abstract
Aircraft operations and the resulting noise emissions have widespread implications that affect numerous stakeholders including airport authorities, airlines, navigation providers, government entities, developers, investors, community members and more. These diverse audiences require information about the degrees of impact of aircraft operations as they relate to noise. The outcome from the use of this information can affect everything from airspace design, zoning, building codes, regulatory measures, public guidance etc. To simplify the subject of aircraft noise, there has always been an effort to use a single metric for the numerous applications listed above. However, this approach more often contributes to confusion and discrepancies as multiple agencies try to force a metric to fit a purpose for which it was not intended. While cumulative noise metrics such as DNL or NEF might be appropriate for annoyance prediction and land-use planning, they are not appropriate for communicating noise data to the public. Likewise, these metrics are not appropriate for building code requirements as they cannot be applied without conversions. Different metrics are developed for different purposes, and they should be applied for their prescribed usage. This paper discusses several metrics and their appropriate applications within the study of aircraft noise and its impacts.
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".