Analysis of community departure noise exposure variation using airport noise monitor networks and operational ADS-B data
Bibliographic record
Abstract
Advanced operational flight procedures have been proposed to reduce the impact of aircraft operations on community noise. Recent work has led to the development of noise abatement procedures like the delayed-deceleration approach for arrivals. Causes of variation in airport noise monitor network measurements due to departures remain an important source of uncertainty in the development of departure noise abatement procedures. Understanding this variation, found to be up to 20 dB at individual monitors for multiple departures, can be accomplished by analyzing aggregate departure noise and flight procedures so statistically-significant factors that correlate with measured noise can be isolated. This paper aims to identify these factors. Operational flights at Seattle-Tacoma International Airport conducted in March and August of 2019 are examined using a framework that includes ADS-B data from the OpenSky Network, a force balance kinematics model to model aircraft performance, and the Seattle-Tacoma International Airport noise monitor network. Variation in measured departure noise throughout the entire monitoring network is examined as a function of aircraft weight, thrust, velocity, specific energy, and flight path angle. Variables that are found to correlate with increased noise are isolated and can be used to inform the development of future departure noise abatement procedures.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".