Characterization Methodology for Biological Plywoods Based on Characteristic Cross-Section Patterns
Bibliographic record
Abstract
Biological plywoods are solid analogues of liquid crystalline phases whose building blocks, including cellulose, collagen and chitin, present multifunctionality, providing in some cases protection, camouflage, self-healing and/or adaptability to the surrounding environment. The 3D ordered structure is the main factor for these fascinating properties, and the assessment of the structure-property relationship will be a powerful tool in terms of future material design and innovation. Cross-section observations lead to characteristic patterns depending on the specific arrangement of the plywood’s building blocks. Twisted plywood architectures, known as the Bouligand structure, lead to the widely observed arced patterns which can be ideal or nonideal depending on whether the relationship between the twist angle and the spatial coordinate is linear or not. The latter is the case of nonideal and the projected arcs to the incision plane do not have a constant periodicity. On the other hand, orthogonal plywoods project into herringbone patterns when the incision angle is adequate. In either case, arcs or herringbones, key characteristic variables, have been identified that provide quantitative means that relate them to structural variables such as the pitch and the helix location. Based on this quantitative information we proposed a methodology to characterize the plywoods when these characteristic patterns are accessible. The method has been validated using in-vivo and in-silico observations, where the latter were obtained using Mayavi, a general purpose 3D visualization software. In this article we present a new analysis of plywoods’ mechanics using Krenchel’s formalism and we give a broad and unifying vision of our recent findings regarding cross-section reconstruction techniques of several biological plywoods along with recommendations that increase accuracy in the predictions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".