Hydrodynamic Drag In Steller Sea Lions (<i>Eumetopias Jubatus</i>)
Bibliographic record
Abstract
Drag forces acting on Steller sea lions (Eumetopias jubatus) were investigated from 'deceleration during glide' measurements. A total of 66 glides from six juvenile sea lions yielded a mean drag coefficient (referenced to total wetted surface area) of 0.0056 at a mean Reynolds number of 5.5x10(6). The drag values indicate that the boundary layer is largely turbulent for Steller sea lions swimming at these Reynolds numbers, which are past the point of expected transition from laminar to turbulent flow. The position of maximum thickness (at 34 % of the body length measured from the tip of the nose) was more anterior than for a 'laminar' profile, supporting the idea that there is little laminar flow. The Steller sea lions in our study were characterized by a mean fineness ratio of 5.55. Their streamlined shape helps to delay flow separation, reducing total drag. In addition, turbulent boundary layers are more stable than laminar ones. Thus, separation should occur further back on the animal. Steller sea lions are the largest of the otariids and swam faster than the smaller California sea lions (Zalophus californianus). The mean glide velocity of the individual Steller sea lions ranged from 2.9 to 3.4 m s(-)(1) or 1.2-1.5 body lengths s(-)(1). These length-specific speeds are close to the optimum swim velocity of 1.4 body lengths s(-)(1) based on the minimum cost of transport for California sea lions.
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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 | 0.000 | 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".