A Multiscale Model to Determine the Stiffness of Collenchyma Tissue in Rheum Rhabarbarum
Bibliographic record
Abstract
The stiffness of plant tissue largely influences the overall mechanical response of plant organs, such as stems, branches and leaf petioles. This work examines the structural hierarchy of the plant tissue; in particular of the collenchyma tissue of the Rheum rhabarbarum. The goal of the paper is to develop a multiscale model capturing features of two orders of its structural hierarchy: cell wall and tissue architecture. The former is considered as a fiber reinforced composite, where the cellulose microfibril (CMF) is the main load bearing component. The longitudinal stiffness of the middle (S2) layer of the secondary cell wall is affected by the microfibril angle (MFA) up to 45° to a greater extent, which in turn plays a role in the overall wall stiffness. The latter, i.e. tissue architecture, influences the tissue stiffness through its random distribution of cells. Finite-edge Centroidal Voronoi Tessellation (FECVT) is used to model the non-periodic microstructure of the rhubarb collenchyma, whose effective elastic properties are obtained through finite element analysis. The results from the FECVT model show that the effective stiffness in the longitudinal direction is 15 to 25% higher than that in the transverse direction for relative density between 5 and 30%. The variation reflects the stiffening effect of the shape and size of the cells in the collenchyma tissue, as well as its aperiodic cellular distribution.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".