Mechanisms of gill-clogging by hagfish slime
Bibliographic record
Abstract
Hagfishes defend themselves from gill-breathing predators by producing large volumes of fibrous slime when attacked. The slime's effectiveness comes from its ability to clog predators' gills, but the mechanisms by which hagfish slime clogs are uncertain, especially given its remarkably dilute concentration of solids. We quantified the clogging performance of hagfish slime over a range of concentrations, measured the contributions of its mucous and thread components, and measured the effect of turbulent mixing on clogging. To assess the porous structure of hagfish slime, we used a custom device to measure its Darcy permeability. We show that hagfish slime clogs at extremely dilute concentrations like those found in native hagfish slime and displays clogging performance that is superior to three thickening agents. We report an extremely low Darcy permeability for hagfish slime, and an effective pore size of 10-300 nm. We also show that the mucous and thread components play distinct yet crucial roles, with mucus being responsible for effective clogging and low permeability and the threads imparting mechanical strength and retaining clogging function over time. Our results provide new insights into the mechanisms by which hagfish slime clogs gills and may inspire the development of ultra-soft materials with novel properties.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".