The effect of bimineralic composition on extensional processes at lithospheric scale
Bibliographic record
Abstract
Abstract We investigate how lithospheric scale compositional heterogeneities affect kilometric scale deformation processes. To this end, we perform numerical experiments of lithospheric extension in which we vary the Moho temperature and the mineralic composition of the mantle and the crust. In both the crust and the mantle, we use an explicit bimineralic composition by randomly distributing two mineral phases in the materials. Comparison of our models to simulations using an implicit bimineralic composite (one average viscous flow laws for a two‐phase aggregate) crust and mantle demonstrates that an explicit bimineralic composition assimilated to heterogeneities succeeds in explaining observations related to the formation of rifted margins such a: (1) the absence of a sharp deformation zone at the brittle ductile transition (BDT), (2) the initiation of the rifting process as a wide delocalized rift system with multiple normal faults dipping in both directions; (3) the development of anastomosing shear zones in the middle/lower crust and the upper lithospheric mantle similar to the crustal scale anastomosing patterns observed in the field or in seismic data; (4) the preservation of undeformed lenses of material leading to lithospheric scale boudinage structure and resulting in the formation of continental ribbons as observed along the Iberian‐Newfoundland margin.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".