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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".