Grain-size distribution in the mantle wedge of subduction zones
Bibliographic record
Abstract
[1] Mineral grain size plays an important role in controlling many processes in the mantle wedge of subduction zones, including mantle flow and fluid migration. To investigate the grain-size distribution in the mantle wedge, we coupled a two-dimensional (2-D) steady state finite element thermal and mantle-flow model with a laboratory-derived grain-size evolution model. In our coupled model, the mantle wedge has a composite olivine rheology that incorporates grain-size-dependent diffusion creep and grain-size-independent dislocation creep. Our results show that all subduction settings lead to a characteristic grain-size distribution, in which grain size increases from 10 to 100 μm at the most trenchward part of the creeping region to a few centimeters in the subarc mantle. Despite the large variation in grain size, its effect on the mantle rheology and flow is very small, as >90% of the deformation in the flowing part of the creeping region is accommodated by grain-size-independent dislocation creep. The predicted grain-size distribution leads to a downdip increase in permeability by ∼5 orders of magnitude. This increase is likely to promote greater upward migration of aqueous fluids and melts where the slab reaches ∼100 km depth compared with shallower depths, potentially providing an explanation for the relatively uniform subarc slab depth. Seismic attenuation derived from the predicted grain-size distribution and thermal field is consistent with the observed seismic structure in the mantle wedge at many subduction zones, without requiring a significant contribution by the presence of melt.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".