Roles of lithospheric strain softening and heterogeneity in determining the geometry of rifts and continental margins. Imaging, Mapping and Modelling Continental Lithosphere Extension and
Bibliographic record
Abstract
Abstract: Plane strain thermo-mechanical finite-element model experiments are used to investi-gate the effects of frictional–plastic strain softening and inherited weakness on the style of litho-spheric extension. The model results are compared with the Newfoundland–Iberia conjugate rifted margins with the goal of understanding the lithospheric properties that controlled their evol-ution during rifting. Our proposition is that coupling between the plastic–viscous layering, acting together with frictional–plastic strain softening localized on inherited weak heterogeneities, can explain the initial wide rift and distributed rift basins that are later abandoned in favour of a narrow rift in which mantle lithosphere is exhumed to the surface. The models comprise uniform composition viscous and plastic layers in which focused deformation is nucleated on either a single weak ‘seed ’ or a statistical white noise distribution of inherited strain. Strain soft-ening of frictional–plastic layers acts as a positive feedback mechanism that creates localized shear zones from the inherited weak heterogeneities. The sensitivity of deformation to the choice of softening parameters and the type of inherited noise is examined in cases where the deeper part of the crust is either weak or strong. Lithosphere-scale models with a single weak seed exhibit a range of asymmetric and symmetric
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".