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Record W2035350680 · doi:10.1242/jeb.02067

Hagfish slime ecomechanics: testing the gill-clogging hypothesis

2006· article· en· W2035350680 on OpenAlexaff
Jeanette Lim, Douglas S. Fudge, Nimrod Levy, John M. Gosline

Bibliographic record

VenueJournal of Experimental Biology · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSkin and Cellular Biology Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHagfishGillCloggingSlime moldBiologyVertebrateFisheryFish <Actinopterygii>Botany

Abstract

fetched live from OpenAlex

Hagfish are able to produce substantial amounts of slime when harassed, but the precise ecological function of the slime is unclear. One possibility is that the slime acts as a defence against gill-breathing predators, whose gills may become entangled with the slime's mixture of mucins and fibrous threads during an attack. We previously demonstrated that hagfish slime does not bind water tightly, but instead behaves like a fine sieve that slows water down via viscous entrainment. These properties are consistent with the gill-clogging hypothesis, which we tested here by quantifying the effects of hagfish slime on water flow through an artificial gill model and real fish gills. Our results indicate that the slime is capable of clogging gills and increasing the resistance that they present to the flow of water. We also characterized the behaviour of slime release from live hagfish and the effect of convective mixing on the formation of slime in vitro. Our observations show that exudate is locally released from the slime glands as a coherent jet and that hagfish do not appear to use their slime as a protective envelope. We found that convective mixing between the exudate and seawater is necessary for proper slime formation, but excessive mixing leads to the slime's collapse. We suggest that the loose binding of water by the slime may be an optimal solution to the problem of delivering an expanding jet of flow-inhibiting material to the gills of would-be 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)0.001
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 score0.014
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.029
GPT teacher head0.286
Teacher spread0.257 · 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

Citations67
Published2006
Admission routes1
Has abstractyes

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