Bibliographic record
Abstract
Motivated by structural heterogeneity and thickness nonuniformity of protein shells (such as microtubules and viral capsids), a refined elastic shell model is suggested to study the effect of transverse shear and effective bending thickness on buckling of an empty spherical viral shell under external pressure. A key feature of the model is that the transverse shear modulus of viral shells is allowed to be much lower than the in-plane shear modulus, in accordance with the weak resistance of two-dimensional protein assemblies to transverse shear. The results show that the transverse shear-induced critical pressure drop could be as big as 50%–70% for smaller-radius viral shells when the transverse shear modulus is about one order of magnitude smaller than the in-plane shear modulus, although the effect of transverse shear is negligible if the transverse shear modulus is equal to or larger than the in-plane shear modulus. These results suggest that the classical homogeneous shell model widely used in the literature would overestimate the strength of viral shells against buckling under external pressure. The refined model suggested here could extend the applicability of homogeneous elastic shell models from larger-radius viral shells to small-radius ones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".