Symmetric and asymmetric lithospheric extension: Relative effects of frictional‐plastic and viscous strain softening
Bibliographic record
Abstract
Strain‐dependent rheologies may play a critical role in the deformation of the lithosphere and particularly in the development of focused shear zones. We investigate the effects of strain softening on lithospheric extension using plane strain thermomechanical finite element model experiments. Parametric softening is specified as a linear decrease of the effective internal angle of friction, the effective viscosity, or both in the model rheologies. The sensitivity of deformation to the choice of softening parameters is investigated in cases where the crust is either strongly or weakly coupled to the mantle lithosphere. Results are classified according to the symmetry (S) or asymmetry (A) of the deformation of the upper and lower lithosphere during rifting. Strain softening is required for rifting asymmetry but is not always sufficient. A range of model tectonic styles occurs including pure and simple shear modes with focused shear zones. Mode selection is mostly determined by the feedback between two primary controls, the “dominant” rheology and the parametric strain‐softening mechanisms listed above. Softening of the dominant rheology promotes asymmetric extension of that part of the lithosphere controlled by the dominant rheology. Model results are consistent with the proposed primary controls and the factors that contribute to these controls. In particular, decreasing and increasing the rifting velocity can change the mode by changing the dominant rheology. Asymmetry is strongest in coupled models which include a decrease in the internal angle of friction and have low rifting velocities.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".