Tail shape and the swimming speed of sharks
Bibliographic record
Abstract
Trait-based ecology is a rapidly growing approach for developing insights and predictions for data-poor species. Caudal tail fin shape has the potential to reveal much about the energetics, activity and ecology of fishes and can be rapidly measured from field guides, which is particularly helpful for data-sparse species. One outstanding question is whether swimming speed in sharks is related to two morphological traits: caudal fin aspect ratio (CFAR, height 2 /tail area) and caudal lobe asymmetry ratio (CLAR). We derived both metrics from the species drawings in Sharks of the world (Ebert et al. 2013 Sharks of the world: a fully illustrated guide ) and related fin shape to two published datasets of (1) instantaneous swimming speeds (Jacoby et al. 2015 Biol. Lett. 11 , 20150781 ( doi:10.1098/rsbl.2015.0781 )) and (2) cruising speeds (Harding et al. 2021 Funct. Ecol. 35 , 1951–1959 ( doi:10.1111/1365-2435.13869 )) for 28 total unique shark species. Both estimates of swimming speed were positively related to CFAR (and weakly negatively to CLAR). Hence, shark species with larger CFAR and more symmetric tails (low CLAR) tended to be faster-moving and have higher average speeds. This relationship demonstrates the opportunity to use tail shape as an easily measured trait to index shark swimming speed to broader trait-based analyses of ecological function and extinction risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".