Fast-start muscle dynamics in the rainbow trout<i>Oncorhynchus mykiss</i>: phase relationship of white muscle shortening and body curvature
Bibliographic record
Abstract
Muscle length changes of the lateral myotomal fast fibers of rainbow trout (Oncorhynchus mykiss) were measured using sonomicrometry during induced fast-starts. Simultaneous high-speed videography allowed for the analysis of midline kinematics to estimate the degree of muscle strain that occurs during body deformation. Comparison of these data was used to examine the phase relationship between local muscle shortening and local body bending during unsteady, large amplitude maneuvers. Our analysis finds that muscle shortening is temporally decoupled from body bending, probably due to the influence of hydrodynamic forces. The phase shift was such that midline curvature lagged behind muscle shortening at both the anterior (0.4 L, where L is fork length) and posterior (0.7 L) axial positions. Stronger escape responses were correlated with high peak strains and rapid strain-wave velocities, but not faster curvature-wave velocities. Under these conditions of high strain, the phase shift at the posterior position is significantly increased, whereas the anterior position fails to be affected. Curvature lag was still observed at both axial locations under conditions of low strain, suggesting that hydrodynamic forces are still significant during weaker escape responses. These data support a previous model that suggests fast-start body bending is determined by the interaction between muscle torque and hydrodynamic resistance along the body.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".