Bibliographic record
Abstract
Natural flexural armors combine hard, discrete scales attached to soft tissues, providing unique combinations of surface hardness (for protection) and flexibility (for unimpeded motion). Scaled skins are now inspiring synthetic protective materials which offer attractive properties, but which still suffer from limited trade-offs between flexibility and protection. In particular, bending a scaled skin with the scales on the intrados side jams the scales and stiffen the system significantly, which is not desirable in systems like gloves where scales must cover the palm side. Nature appears to have solved this problem by creating scaled skins that can form wrinkles and folds, a very effective mechanism to accommodate large bending deformations and to maintain flexural compliance. This study is inspired from these observations: we explored how rigid scales on a soft membrane can buckle and fold in a controlled way. We examined the energetics of buckling and stability of different buckling modes using a combination of discrete element modeling and experiments. In particular, we demonstrate how scales can induce a stable mode II buckling, which is required for the formation of wrinkles and which could increase the overall flexural compliance and agility of bioinspired protective elements.
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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 | 0.000 |
| 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".