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Record W2576397849 · doi:10.1242/jeb.151233

Hagfish Houdinis: biomechanics and behavior of squeezing through small openings

2017· article· en· W2576397849 on OpenAlexafffund
Calli R. Freedman, Douglas S. Fudge

Bibliographic record

VenueJournal of Experimental Biology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHagfishBiomechanicsAnatomyTerrestrial locomotionBiologyVertebrate

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.372
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2017
Admission routes2
Has abstractyes

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