Diverse anguilliform swimming kinematics in Pacific hagfish (<i>Eptatretus</i><i> stoutii</i>) and Atlantic hagfish (<i>Myxine</i><i> glutinosa</i>)
Bibliographic record
Abstract
Anguilliform mode swimmers pass waves of lateral bending down their elongate bodies to propel forward. Hagfishes (Myxinidae) are classified as anguilliform swimmers, but their unique habits and reduced morphology—including a flexible body lacking a vertebral column—have the potential to translate into unique swimming behaviour within this broad classification. Their roles as active scavengers and hunters can require considerable bouts of swimming, yet quantitative data on hagfish locomotion are limited. Here, we aim to provide a more complete mechanistic understanding of hagfish swimming by quantifying whole-body kinematics of steady swimming in Pacific hagfish (Eptatretus stoutii (Lockington, 1878)) and Atlantic hagfish (Myxine glutinosa L., 1758), species from the two main lineages of Myxinidae. We analyzed high-speed video of hagfishes swimming at voluntary swim speeds and found that both species swim using high-amplitude undulatory waves. Swim speed is generally frequency-modulated, but patterns in wave speed, wavelength, and amplitude along the body and across swim speeds are variable, implying versatile mechanisms for the control of swim speed in these highly flexible fishes. We propose mechanistic explanations for this kinematic variability and compare hagfish with other elongate swimmers, demonstrating that the hagfish’s rich locomotory repertoire adds variety to the already diverse set of locomotory kinematics found in anguilliform swimmers.
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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 | 0.001 | 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) | 0.001 | 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".