Morphogenesis through mechanical instabilities : wrinkles and rufflesformation during the growth of kelp blades
Bibliographic record
Abstract
Brown macroalgae, also known as kelp, consist of a holdfast that anchors them to the sea floor, a stipe that rises to the water surface, and leaf-like structures known as blades whose primary function is photosynthesis. A close inspection of the blades' surface reveals complex and diverse geometrical features, from tiny wrinkles to large ruffles. It has been demonstrated that the edges of kelp blades grow faster than their midline, inducing a global plate instability known as ruffling. However, morphogenesis of the kelp blade could also be linked to the differential growth across the thickness of the blade. Indeed, the outer layers (i.e, the meristoderm) grow through cell proliferation while pulling on a passive inner core (i.e., the medulla and cortex). This incompatibility in growth eventually leads to a surface instability called wrinkling which has mostly been characterized for bi-layers systems. Here, we model the kelp blades as a tri-layer to study the influence of wrinkling instabilities on its morphogenesis. Using a combination of reduced-order models and finite element simulations, we characterize the influence of material and geometrical parameters (e.g., layers' modulus, thickness, boundary conditions) on the wrinkling onset and the complex, post-instability deformation of the system. Our results shed light onto the role of wrinkling in the morphogenesis of kelp blade and could be extended to induce wrinkles and ruffles in artificial systems.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".