A comparison of mantle convection models featuring plates
Bibliographic record
Abstract
Abstract Oceanic plates are an integral part of the Earth's mantle and thus play an important role in its dynamics and evolution. To allow plate behavior to arise naturally in numerical mantle convection models, self‐consistent plate generation methods apply a fully rheological approach (featuring a temperature‐, pressure‐ and stress‐dependent viscosity). However, due to the extreme local viscosity changes that the self‐generation of model plates entails, their computational requirements are demanding. Alternative plate modeling methods specify the existence of plates explicitly but can also obtain dynamically determined velocities (e.g., by employing a force‐balance method). Here we present modifications to a force‐balance model by utilizing a rheology‐dependent viscosity profile. Accordingly, plate viscosity and plate thickness are no longer prescribed by the modeler but now follow as a dynamic consequence of the temperature and stress dependence of the viscosity and the model's evolution. We describe the new method and present benchmark results for a rheologically self‐consistent mantle convection model and the modified force‐balance plate model. Our results show that both plate modeling methods lead to the same system behavior for a wide range of system parameters making the new method a powerful tool to also achieve plate‐like surface motion naturally.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".