Tail shape and the swimming speed of sharks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it