Bibliographic record
Abstract
Current U.S. and international laws prohibit commercial supersonic flight over land due to the impact of conventional sonic boom noise. Aircraft manufacturers, however, now have modern computational fluid dynamics and optimization tools, unavailable when those laws were enacted, that will allow them to design and build aircraft with boom signatures that are substantially smoothed compared with traditional N-waves. One purpose of the FAA/NASA/Transport Canada PARTNER Center of Excellence Project 8 is to determine exactly which waveforms would be heard by the public if low-boom supersonic aircraft are put into service. Another purpose is to ascertain the acceptability of those waveforms. The project involves the following universities, government, and industry partners: Penn State, Purdue, Stanford, the National Aeronautics and Space Administration, the Federal Aviation Administration, Boeing, Cessna, Gulfstream, Lockheed-Martin, and Wyle Laboratories. Some of the initial project work includes studies on the propagation of sonic booms through atmospheric turbulence, on the mutual reproducibility of three sonic boom simulators, and on the realism of those simulators as determined by expert listeners. The results of all the studies are intended to provide the FAA with new data to reassess current regulations. [Work supported by NASA, the FAA, and the PARTNER industrial partners.]
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".