Drag, but not buoyancy, affects swim speed in captive Steller sea lions
Bibliographic record
Abstract
Swimming at an optimal speed is critical for breath-hold divers seeking to maximize the time they can spend foraging underwater. Theoretical studies have predicted that the optimal swim speed for an animal while transiting to and from depth is independent of buoyancy, but is dependent on drag and metabolic rate. However, this prediction has never been experimentally tested. Our study assessed the effects of buoyancy and drag on the swim speed of three captive Steller sea lions (Eumetopias jubatus) that made 186 dives. Our study animals were trained to dive to feed at fixed depths (10-50 m) under artificially controlled buoyancy and drag conditions. Buoyancy and drag were manipulated using a pair of polyvinyl chloride (PVC) tubes attached to harnesses worn by the sea lions, and buoyancy conditions were designed to fall within the natural range of wild animals (∼12-26% subcutaneous fat). Drag conditions were changed with and without the PVC tubes, and swim speeds were recorded and compared during descent and ascent phases using an accelerometer attached to the harnesses. Generalized linear mixed-effect models with the animal as the random variable and five explanatory variables (body mass, buoyancy, dive depth, dive phase, and drag) showed that swim speed was best predicted by two variables, drag and dive phase (AIC = -139). Consistent with a previous theoretical prediction, the results of our study suggest that the optimal swim speed of Steller sea lions is a function of drag, and is independent of dive depth and buoyancy.
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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".