Drag-Based ‘Hovering’ in Ducks: The Hydrodynamics and Energetic Cost of Bottom Feeding
Bibliographic record
Abstract
Diving ducks use their webbed feet to provide the propulsive force that moves them underwater. To hold position near the bottom while feeding, ducks paddle constantly to resist the buoyant force of the body. Using video sequences from two orthogonal cameras we reconstructed the 3-dimensional motion of the feet through water and estimated the forces involved with a quasi-steady blade-element model. We found that during station holding, near the bottom, ducks use drag based propulsion with the webbed area of the foot moving perpendicular to the trajectory of the foot. The body was pitched at 76+/-3.47 degrees below the horizon and the propulsive force was directed 26+/-1.9 degrees ventral to the body so that 98% of the propulsive force in the sagittal plane of the duck worked to oppose buoyancy. The mechanical work done by moving both feet through a paddling cycle was 1.1+/-0.2 J which was equivalent to an energy expenditure of 3.7+/-0.5 W to hold position while feeding at 1.5 m depth. We conclude that in shallow water the high energetic cost of feeding in ducks is due to the need to paddle constantly against buoyancy even after reaching the bottom. The mechanical energy spent on holding position near the bottom, while feeding, is approximately 2 fold higher than previous estimates that were made for similar bottom depths but based on the presumed motion of the body instead of motion of the feet.
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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 | 0.000 |
| 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 | 0.001 | 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".