In‐flight force estimation by flight mill calibration
Bibliographic record
Abstract
Abstract The study of insect flight is important for conservation and sustainability efforts, as predicting insect dispersal can aid management programmes in tackling economic and ecological harm from, for example, invasive species. Flight mills are invaluable tools for measuring the factors of insect flight under laboratory conditions, as they lower several technical and financial barriers to conduct experiments. It is especially difficult, however, to make assumptions about the energetic cost of tethered flights conducted using different tethers, or even on different flight mills, due to the mechanical variability of the bearing friction and air resistance of the rotating assembly. This additional uncertainty necessitates a larger number of replicates for any given standard of statistical confidence. By characterising flight mill friction, this uncertainty can both be reduced in magnitude and assigned a specific, well‐defined numerical value. We present a simple methodology to characterise this friction through dynamic calibration of the flight mill, at a high statistical confidence. This study uses videography of a flight mill undergoing free velocity decay due to friction, using an in‐house developed software to extract angular velocity from video data. However, the technique is readily adaptable to other measurement techniques. Using the velocity, alongside the mass moment of inertia of the flight mill, allows us to determine the rotational friction coefficient. This friction coefficient provides precise measurements of thrust production, and therefore the energy expenditure of flight, by the tethered insect.
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How this classification was reachedexpand
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".