Dissipation analysis as a guide to mode selection during crustal extension and implications for the styles of sedimentary basins
Bibliographic record
Abstract
We analyze the initial modes of continental extension with the aim of providing an improved understanding of the dynamic development of sedimentary basins. We first examine simple two‐layer crustal‐scale models which consist of a frictional‐plastic upper crust bonded to a linear viscous lower crust of equal thickness. The mode of deformation is predicted by using an analytical analysis of the rate of internal dissipation of energy and the gravitational rate of work. It is assumed that models deform in the mode in which the total rate of work is minimized. For strain‐softening models we predict the following modes of crustal extension: (1) pure shear, (2) multiple conjugate or parallel shear zones, (3) two shear zones, which form either one symmetric basin or two asymmetric basins, and (4) a single shear zone forming an asymmetric basin. The transitions between these modes are shown to depend on the trade off between “gains” that reduce the rate of energy dissipation and “penalties” that increase it. A single asymmetric basin is preferred for a strong brittle layer which has a high amount of strain softening (high plastic gain), a weak viscous layer and slow extension (low viscous penalty). A decrease in the plastic gain and/or an increase in the viscous penalty leads to modes with more shear zones in the upper crust. The pure shear mode is found for low strain softening, a high viscosity, and/or fast extension. Results of finite element calculations of equivalent simple two‐layer models agree with the analytical mode predictions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".