Bibliographic record
Abstract
To increase the efficiency of aircraft development, a simulation has been planned-out to test wing-shape in a virtual environment. The simulation tests the efficiency of swept wings, which are angled towards the tail of an airplane, or the efficiency of forward swept wings, angled towards the nose of the airplane. The simulation involves parameters to mimic real-world effects on virtual aircraft designs. Such simulations have been used by Boeing to replace the wind tunnel, saving time, money, and lives. In the future, such simulations may eliminate the hindrances of testing what wing types belong on what aircraft. Pour augmenter l'efficacité du développement de l'aéronef, une simulation a été conçue pour tester les configurations d'aile dans un environnement virtuel. La simulation teste l'efficacité des ailes en flèche, qui sont inclinées vers la queue d'un avion, ou de l'efficacité des ailes en flèche vers l'avant, inclinée vers le nez de l'avion. La simulation imite les effets du monde réel sur un avion virtuel en utilisant plusieurs paramètres. Ces simulations ont été utilisées par Boeing pour remplacer la soufflerie, économisant du temps, de l'argent, et des vies. Dans l'avenir, ces simulations peuvent éliminer les obstacles de tester quels types d'ailes appartiennent à quel avion.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.009 | 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".