Microstructural computational modelling of soft tissues
Bibliographic record
Abstract
In this work we numerically implement a previously developed large deformation model for porous fibre-reinforced materials with statistically oriented fibres, including the effect of the presence of the fibres on both the elastic properties and the permeability. The model is microstructural, based on observations made at different length-scales: the microscopic scale of the porous matrix, the mesoscopic scale of the reinforcing fibres, and the macroscopic scale of the system as a whole, and makes use of upscaling techniques. The implementation makes use of the open-source Finite Element package FEBio, which allows for full customisation of the constitutive equations. We first study a benchmark test in which only the effect of the fibre orientation on the permeability is accounted for. Then we simulate an unconfined compression test on a sample of articular cartilage, with realistic histological features, such as the volumetric fractions of the matrix and collagen fibres and fibre orientation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".