Dynamic elastocapillary coalescence of fish gill lamellae
Bibliographic record
Abstract
Aquatic animals like fishes and larval amphibians have flexible gills with a large surface area for gas exchange. When exposed to air, gills typically collapse and coalesce due to the elastocapillary effect, reducing gas exchange and potentially causing death. To resist these effects, some amphibians are hypothesized to have evolved stiffened gills, but these elastocapillarity effects have not been investigated empirically or theoretically. Here, we examine the deformations of artificial elastomeric gill lamellae under quasi-static and dynamic liquid crossing scenarios, inspired by conditions faced by amphibious animals when leaving water. First, we discovered multiple equilibrium states when the liquid interface is pinned to the lamellae tips, where lamellae either coalesce or remain separated depending on the liquid volume constraints. Moreover, we observe a unidirectional collapse pattern, termed the 'dominos pattern', under spatially variant drainage rate. A reduced-order dynamic model provides quantitative insights into these equilibria based on the lamellae properties, liquid volumes and drainage conditions leading to dominos patterns. These results inspire novel hypotheses about how elastocapillary may influence the evolution of gill structure in amphibious species, and also provide bioinspiration for engineering applications such as polymorphic display devices using flexible lamellae.
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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 | 0.000 |
| 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".