Hydrodynamic Drag of Diving Birds: Effects Of Body Size, Body Shape And Feathers At Steady Speeds
Bibliographic record
Abstract
For birds diving to depths where pressure has mostly reduced the buoyancy of air spaces, hydrodynamic drag is the main mechanical cost of steady swimming. Drag is strongly affected by body size and shape, so such differences among species should affect energy costs. Because flow around the body is complicated by the roughness and vibration of feathers, feathers must be considered in evaluating the effects of size and shape on drag. We investigated the effects of size, shape and feathers on the drag of avian divers ranging from wing-propelled auklets weighing 75 g to foot-propelled eiders weighing up to 2060 g. Laser scanning of body surfaces yielded digitized shapes that were averaged over several specimens per species and then used by a milling machine to cut foam models. These models were fitted with casts of the bill area, and their drag was compared with that of frozen specimens. Because of the roughness and vibration of the feathers, the drag of the frozen birds was 2-6 times that of the models. Plots of drag coefficient (C(D)) versus Reynolds number (Re) differed between the model and the frozen birds, with the pattern of difference varying with body shape. Thus, the drag of cast models or similar featherless shapes can differ both quantitatively and qualitatively from that of real birds. On the basis of a new towing method with no posts or stings that alter flow or angles of attack, the dimensionless C(D)/Re curves differed among a size gradient of five auklet species (75-100g) with similar shapes. Thus, extrapolation of C(D)/Re curves among related species must be performed with caution. At lower speeds, the C(D) at a given Re was generally higher for long-necked birds that swim with their neck extended (cormorants, grebes, some ducks) than for birds that swim with their head retracted (penguins, alcids), but this trend was reversed at high speeds. Because swimming birds actually travel at a range of instantaneous speeds during oscillatory strokes, species variations in drag at different speeds must be considered in the context of accelerational stroking.
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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 | 0.000 | 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) | 0.001 | 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".