Hagfish Houdinis: biomechanics and behavior of squeezing through small openings
Bibliographic record
Abstract
Hagfishes are able to squeeze through small openings to gain entry to crevices, burrows, hagfish traps, and carcasses, but little is known about how they do this, or what the limits of this ability are. The purpose of this study was to describe this ability, and to investigate possible mechanisms by which it is accomplished. We investigated the hypothesis that the passive movement of blood within a hagfish's flaccid subcutaneous sinus allows it to squeeze through narrow apertures that it would not be able to if it were turgid. To test this hypothesis, we analyzed videos of Atlantic hagfish (Myxine glutinosa) and Pacific hagfish (Eptatretus stoutii) moving through narrow apertures in the lab. We measured changes in body width as the animals moved through these openings, and documented the behaviors associated with this ability. We found that hagfishes are able to pass through narrow slits that are less than one half the width of their bodies. Our results are consistent with the idea that a flaccid subcutaneous sinus allows hagfish to squeeze through narrow apertures by facilitating a rapid redistribution of venous blood. In addition, we describe nine distinct behaviors associated with this ability, including a form of non-undulatory locomotion also seen in snakes and lampreys. Our results illuminate a behavior that may be a critical component of the hagfish niche, due to its likely importance in feeding and avoiding predators.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".