Variations in Grain Size and Viscosity Based on Vacancy Diffusion in Minerals, Seismic Tomography and Geodynamically Inferred Mantle Rheology
Bibliographic record
Abstract
The effect of grain size on mantle viscosity is comparable to that of temperature and pressure. The current 3-D distribution of grain size in the mantle is, however, unknown. To explore the possible variability of grain size, we use the following: geodynamic inferences of effective viscosity, vacancy diffusion rates in upper mantle minerals and perovskite in the lower mantle, lateral variations in temperature derived from seismic images, and different geotherms. An important outcome of this modeling is a new mapping of lateral viscosity variations throughout the mantle. The corresponding 3-D variations in grain size are characterized by 2 orders of magnitude changes. We find a correlation between grain size variability in the mantle and absolute viscosity changes with depth. Our findings suggest that the traditional assumption of Arrhenius temperature dependence for vacancy diffusion in the lower mantle is not sufficient to constrain the deformation mechanisms that determine its effective bulk viscosity.
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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 | 0.001 | 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) | 0.000 | 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".