Flexibility is a hidden axis of biomechanical diversity in fishes
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
Nearly all fish have flexible bodies that bend as a result of internal muscular forces and external fluid forces that are dynamically coupled with the mechanical properties of the body. Swimming is therefore strongly influenced by the body's flexibility, yet we do not know how fish species vary in their flexibility and in their ability to modulate flexibility with muscle activity. A more fundamental problem is our lack of knowledge about how any of these differences in flexibility translate into swimming performance. Thus, flexibility represents a hidden axis of diversity among fishes that may have substantial impacts on swimming performance. Although engineers have made substantial progress in understanding these fluid-structure interactions using physical and computational models, the last biological review of these interactions and how they give rise to fish swimming was carried out more than 20 years ago. In this Review, we summarize work on passive and active body mechanics in fish, physical models of fish and bioinspired robots. We also revisit some of the first studies to explore flexural stiffness and discuss their relevance in the context of more recent work. Finally, we pose questions and suggest future directions that may help reveal important links between flexibility and swimming performance.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 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