Identifying mantle lithosphere inheritance in controlling intraplate orogenesis
Bibliographic record
Abstract
Abstract Crustal inheritance is often considered important in the tectonic evolution of the Wilson Cycle. However, the role of the mantle lithosphere is usually overlooked due to its difficulty to image and uncertainty in rheological makeup. Recently, increased resolution in lithosphere imaging has shown potential scarring in continental mantle lithosphere to be ubiquitous. In our study, we analyze intraplate deformation driven by mantle lithosphere heterogeneities from ancient Wilson Cycle processes and compare this to crustal inheritance deformation. We present 2‐D numerical experiments of continental convergence to generate intraplate deformation, exploring the limits of continental rheology to understand the dominant lithosphere layer across a broad range of geological settings. By implementing a “jelly sandwich” rheology, common in stable continental lithosphere, we find that during compression the strength of the mantle lithosphere is integral in generating deformation from a structural anomaly. We posit that if the continental mantle is the strongest layer within the lithosphere, then such inheritance may have important implications for the Wilson Cycle. Furthermore, our models show that deformation driven by mantle lithosphere scarring can produce tectonic patterns related to intraplate orogenesis originating from crustal sources, highlighting the need for a more formal discussion of the role of the mantle lithosphere in plate tectonics.
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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".