Optimal fineness ratio for minimum drag in large whales
Bibliographic record
Abstract
The optimum fineness ratio (X = L/d, where L and d are body length and profile height, respectively) for minimum drag is about 4.5 and many fast swimming fish are characterized by values of this order. However, values for large whales that undergo extensive migrations (e.g., Balaenopteridae, Balaenidae, and Physeteridae) are as high as 8. A plot of fineness ratio versus mass (M) for different species of large whales shows that the optimal fineness ratio for minimum drag and therefore the minimum cost of transport increases slowly with increasing mass (X = 4M0.06). Optimal fineness ratio was determined from a simple hydromechanical model based on the sum of friction and pressure drag on an equivalent cylindrical body, which indicate a small positive dependence (0.11) of optimal fineness ratio for minimum drag with increasing body mass, suggesting an adaptation for reducing the energy cost of swimming.
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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".