Atmospheric turbulence filter functions derived from high-fidelity measurements
Bibliographic record
Abstract
Efforts have been underway to develop filter functions suitable for adding turbulent atmospheric effects to theoretical low-boom waveforms. Filter functions have been created based on a measured input-output relationship. The input is a relatively clean sonic boom waveform measured at altitude by a glider, and the outputs are turbulized’ waveforms measured on the ground. One input waveform and multiple output waveforms are used to represent multiple realizations of the atmosphere. Work presented in 2005 [Locey and Sparrow, Innovations in Nonlinear Acoustics, 17th International Symposium on Nonlinear Acoustics (American Institute of Physics, Melville, NY, 2006)] yielded an initial set of filter functions using one particular algorithm and data collected during the Shaped Sonic Boom Experiment (SSBE) in January of 2004. In this talk new results will be presented based on high-fidelity measurements made at NASA Dryden Flight Research Center in June of 2006 [T. Gabrielson et al., Proc. Internoise (2006)]. Time permitting, additional methods for obtaining filter functions will be discussed, including the use of existing sonic boom propagation codes modified to include atmospheric turbulence. [Work supported by the FAA/NASA/Transport-Canada PARTNER Center of Excellence for Aircraft Noise and Aviation Emissions Mitigation.]
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".