Bibliographic record
Abstract
A generalized damage parameterization is developed for the Maxwell Elasto-Brittle (MEB) rheology that reduces the growth of residual errors associated with the correction of super-critical stresses. In the generalized stress correction, a decohesive stress tensor is used to bring the super-critical stresses back on the yield curve based on any correction path. The sensitivity of the simulated material behaviour to the magnitude of the decohesive stress tensor is investigated in uniaxial compression simulations. Results show that while the decohesive stress tensor influences the short-term fracture deformation and orientation, the long-term post-fracture behaviour remains unchanged. Divergence first occurs when the elastic response is dominant followed by post-fracture shear and convergence when the viscous response dominates – contrary to laboratory experiment of granular flow and satellite imagery in the Arctic. The post-fracture deformations are shown to be dissociated from the fracture process itself, an important difference with classical Viscous Plastic (VP) models. Using the generalized damage parameterization together with a stress correction path normal to the yield curve brings the simulated fracture angles closer to observations (from 40–50° to 35–45°, compared to 20–30° in observations) and reduces the growth of errors sufficiently for the production of longer-term simulations.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.578 | 0.619 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".