Subduction Zone Obliquity Dictates Global Trench‐Parallel Inner Forearc Deformation
Bibliographic record
Abstract
Abstract Although subduction zones are characterized by convergence, the upper plates of subduction zones exhibit a diverse range of deformation styles that are often inconsistent with regional convergence. While several theories have been proposed to explain these variations, the underlying factors driving these differences are still not fully understood. In this study, we analyze 24,000 km of active global subduction zones around the globe to determine how subduction zone obliquity affects deformation in the trench‐parallel and horizontal directions on land above subduction zones. We take advantage of recently published worldwide data sets of Global Navigation Satellite System (GNSS) velocities and global active fault catalogs in order to examine deformation at 13 of the world's forearcs. We analyze deformation over both short (decadal) timescales, captured by GNSS, and long (millennial to million‐year) timescales, observed through trench‐parallel active forearc faults. The results reveal a strong link between subduction obliquity and both the sense and amount of forearc rotation detected by GNSS, as well as the sense and rate of deformation along trench‐parallel strike‐slip faults. Unlike previous studies indicating that subduction obliquity affects forearc deformation only beyond a certain threshold, we demonstrate that even low to moderate obliquity significantly influences the observed deformation.
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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 | 0.000 | 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".