Determining a free flight performance surface by mathematical optimization techniques utilizing an air speed indicator, MEMS inertial sensors and a variomete
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Paragliding is unpowered flight in which pilots rely on their ability to navigate rising currents of air to remain airborne. Paraglider flight performance is an important measure of the capabilities of a particular design of a canopy. Most often, the performance characteristics of a canopy are measured as horizontal velocity vs. vertical velocity for steady state flight in still air. The performance curve created using this approach neglects to take into account the effect which turning has on flight. In contrast, the performance surface created from the research carried out in this paper demonstrates the effect of turning on canopy flight; such a representation of performance is novel to the authors' knowledge. To produce this surface, a flight was conducted in which a paraglider's performance was measured for various steady state velocities and turning rates; the data were then analyzed utilizing mathematical optimization after appropriate calibration corrections were applied.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it