Integrating morphology and kinematics in the scaling of hummingbird hovering metabolic rate and efficiency
Bibliographic record
Abstract
Wing kinematics and morphology are influential upon the aerodynamics of flight. However, there is a lack of studies linking these variables to metabolic costs, particularly in the context of morphological adaptation to body size. Furthermore, the conversion efficiency from chemical energy into movement by the muscles (mechanochemical efficiency) scales with mass in terrestrial quadrupeds, but this scaling relationship has not been demonstrated within flying vertebrates. Positive scaling of efficiency with body size may reduce the metabolic costs of flight for relatively larger species. Here, we assembled a dataset of morphological, kinematic, and metabolic data on hovering hummingbirds to explore the influence of wing morphology, efficiency, and mass on hovering metabolic rate (HMR). We hypothesize that HMR would decline with increasing wing size, after accounting for mass. Furthermore, we hypothesize that efficiency will increase with mass, similarly to other forms of locomotion. We do not find a relationship between relative wing size and HMR, and instead find that the cost of each wingbeat increases hyperallometrically while wingbeat frequency declines with increasing mass. This suggests that increasing wing size is metabolically favourable over cycle frequency with increasing mass. Further benefits are offered to larger hummingbirds owing to the positive scaling of efficiency.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".