Turning trade‐offs: hummingbird power reserves are used to decrease turning radius or increase turning velocity
Bibliographic record
Abstract
Abstract Hummingbirds use their extreme maneuverability to defend territories and win competitions. In theory, a bird can tap into its muscular power reserves to perform complex maneuvers, with the size of the power reserves dictating the maximum maneuvering performance. To test the link between power reserves and maximum maneuvering performance, we used load‐lifting trials to measure the power reserves of Anna's hummingbirds (Calypte anna). Based on these estimates, we calculated the theoretical maximum arcing turn performance. Finally, we used thousands of arcing turns measured with an automated tracking system to evaluate whether maximum turning ability aligned with the theoretical predictions. The maximum turning performance of the hummingbirds closely matched the maximum predicted by their power reserves, even though individual performance maximums were not correlated with individual power reserves. Therefore, our evidence that power reserves underlie maximum performance is mixed: it is in the aggregated turns across all individuals that the large‐scale patterns of maximal performance begin to emerge. Because they limited turning performance, power reserves also created a trade‐off between radius and velocity. As large free‐flight datasets continue to be explored, it is likely that we will continue to find associations between burst power and maximal maneuvering performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".