Measuring low-altitude wind gusts using the unmanned aerial vehicle GustAV
Bibliographic record
Abstract
A small unmanned aerial vehicle (SUAV) was developed with the specific objective to explore atmospheric wind gusts at low altitudes below 500 m. These gusts have significant impact on the flight characteristics and performance of SUAVs. The SUAV carried an advanced air-data system that includes a five-hole probe, which was adapted for this specific application. In several flight tests the entire test system was qualified and gust data were recorded. The subsequent experimentally derived gust data were post-processed and compared with turbulence spectra of the MIL-HDBK-1797 von Kármán turbulence model. On the day of the flight test, the experimental results did not fully match the prediction of the von Kármán model. Meanwhile, the wind measuring apparatus were proven to be able to measure gust during flight. Therefore, a broader sampling will be required to generalize the gust measurements and be compared with the existing models.
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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 | 0.000 | 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 | 0.000 |
| 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 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".