Acoustic Atmospheric Propagation Model Validation with the NRC Convair 580
Bibliographic record
Abstract
The National Research Council Canada was tasked to concurrently assess the visibility and audibility of aircraft in the vicinity of low-traffic aerodromes. This paper discusses the efforts to numerically estimate the acoustic propagation of an aircraft’s acoustic signature using the Impedance Plane Formulation and the Fast Field Program. The experimental methodology includes the measurement of lowaltitude aircraft fly-by acoustic spectra as reference data and high-altitude aircraft fly-by acoustic spectra as target data for the acoustic propagation models. It is shown that the selected acoustic atmospheric propagation models are able to accurately estimate certain frequency components of the acoustic spectra for low and high altitudes of flight of the Convair 580 aircraft. Signature aircraft tones located at 70 Hz and 140 Hz are identified by the models and accurately estimated in location and amplitude. The models’ performance is evaluated to a range of 8 km in the horizontal plane and 3.66 km in altitude; the models exhibited reduced performance as a function of distance. A rudimentary subjective analysis was completed to inform future work; results were consistent with the objective analysis.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".