Measuring power input, power output and energy conversion efficiency in un-instrumented flying birds
Bibliographic record
Abstract
Cost of flight at various speeds is a crucial determinant of flight behavior in birds. Aerodynamic models, predicting that mechanical power (Pmech) varies with flight speed in a U-shaped manner, have been used together with an energy conversion factor (efficiency) to estimate metabolic power (Pmet). Despite few empirical studies, efficiency has been assumed constant across flight speeds at 23%. Ideally, efficiency should be estimated from measurements of both Pmech and Pmet in un-instrumented flight. Until recently, progress has been hampered by methodological constraints. The main aim of this study was to evaluate recently developed techniques and estimate flight efficiency across flight speeds. We used the 13C-labeled sodium bicarbonate method (NaBi) and Particle Image Velocimetry (PIV) to measure Pmet and Pmech in blackcaps flying in a wind tunnel. We also cross validated measurements made by NaBi with Quantitative Magnetic Resonance (QMR) body composition analysis in yellow-rumped warblers. We found that Pmet estimated by Nabi was ∼12% lower than corresponding values estimated by QMR. Pmet varied in a U-shaped manner across flight speeds in blackcaps, but the pattern was not statistically significant. Pmech could only be reliably measured for two intermediate speeds and estimated efficiency ranged between 14 and 22% (combining the two speeds for raw and weight/lift specific power, with and without correction for the ∼12% difference between NaBi and QMR) were close to the currently used default value. We conclude that NaBi and PIV are viable techniques, allowing researchers to address some of the outstanding questions regarding bird flight energetics.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".