Curved orogenic belts, back-arc basins, and obduction as consequences of collision at irregular continental margins
Bibliographic record
Abstract
Abstract Continental collisions commonly involve highly curved passive plate margins, leading to diachronous continental subduction during trench rollback. Such systems may feature back-arc extension and ophiolite obduction postdating initial collision. Modern examples include the Alboran and Banda arcs. Ancient systems include the Newfoundland and Norwegian Caledonides. While external forces or preexisting weaknesses are often invoked, we suggest that ophiolite obduction can equally be caused by internal stress buildup during collision. Here, we modeled collision with an irregular subducting continental margin in three-dimensional (3-D) thermo-mechanical models and used the generated stress field evolution to understand resulting geologic processes. Results show how tensional stresses are localized in the overriding plate during the diachronous onset of collision. These stresses thin the overriding plate and may open a back-arc spreading center. Collision along the entire trench follows rapidly, with inversion of this spreading center, ophiolite obduction, and compression in the overriding plate. The models show how subduction of an irregular continental margin can form a highly curved orogenic belt. With this mechanism, obduction of back-arc oceanic lithosphere naturally evolves from a given initial margin geometry during continental collision.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".