A semi-analytical ray method to predict the propagation of long-range vertical noise: Application of NORD 2000 to the prediction of aircraft en-route noise
Bibliographic record
Abstract
In the past, considerable efforts were made into developing models for the prediction of short-range propagation, in the context of airport noise (Integrated Noise Model, FAA) or more generally for short-range horizontal community noise propagation (NORD 2000, Delta Inc.). Recent research efforts have focused on the prediction of aircraft en-route noise for flights above 18 000 feet above ground level (AGL) (5.49 km AGL), in order to estimate the noise impact in U.S. National Parks and other quiet areas. Unlike other community noise issues, long-range vertical propagation requires an altitude-stratified and realistic atmosphere, which directly impacts the geometrical and absorption losses as well as the ground effects. Given the large distances involved, the use of a semi-analytical propagation model based on the ray theory could prove to be useful in reducing the computation time. NORD 2000, based on semi-analytical ray theory, was modified for use with aircraft en-route noise. A comparative study against AERNOM (Advanced En Route NOise Module, based on the numerical ray method) from Penn State is presented. [Work supported by VOLPE National Transportation Systems Center. The findings are the views of the authors and do not necessarily reflect the views of VOLPE, the FAA, NASA, or Transport Canada.]
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".