Fish locomotor variation: connecting energetics and kinematic modulation
Bibliographic record
Abstract
Abstract Analyses of vertebrate locomotion have frequently revealed variations in locomotor energetics and movement both among individuals and through time within an individual. This variation is often collapsed into mean values for broad comparative analyses of function. However, kinematic patterns of locomotion, even when animals move at a near-constant mean speed, frequently vary with both the physical and biological context. Here we demonstrate, using analyses of fish locomotion and energetics, how variation among individuals in kinematic gaits can manifest as changes in dynamics of metabolic rate (estimated from oxygen uptake). We present kinematic data from a small school of giant danio ( Devario aequipinnatus ) to show that fish within a school frequently modulate their kinematics and change position, even when the school moves at an overall constant mean speed. We show that rainbow trout ( Oncorhynchus mykiss ), swimming over a range of speeds, exhibit considerable variation in tail beat frequency and metabolic rate among speeds. By experimentally altering the fluid dynamic environment, we demonstrate that brook trout ( Salvelinus fontinalis ) show correlated modulation of both kinematics and metabolic rate. Simultaneous measurement of energetic and biomechanical characteristics can unveil the physiological, biomechanical, and fluid dynamic mechanisms that underlie dynamic changes in vertebrate locomotor gaits.
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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 | 0.001 |
| 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".