Bibliographic record
Abstract
The FAA has funded a great deal of research related to the impacts of aviation noise on communities, which has led to the FAA undertaking a review of the Civil Aviation Noise Policy. However, before FAA determines any changes in noise policy, additional research is needed. Our paper discusses the work that ACI-NA - in the role of representing local, regional and state governing bodies that own and operate commercial airports in the United States and Canada - has undertaken, and the areas for further research that we identified in the ACI-NA comment letter submitted to the Federal Register in September 2023 in response to the Federal Aviation Administration's (FAA) Review of the Civil Aviation Noise Policy, (Docket ID No. FAA-2023-0855, 86 Fed. Reg. 26641 (May 1, 2023)). Our paper will discuss our comments, which include policy considerations, as well as specific areas that we recommend for future research, such as additional precision in the causes of annoyance from aviation noise, and why we think that additional research is needed.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".