Time dependence in 3‐D mantle convection models featuring evolving plates: Effect of lower mantle viscosity
Bibliographic record
Abstract
Evolving plate configurations and dynamically determined plate velocities are featured in Cartesian geometry mantle convection simulations. The numerical model enables the evolution of plate shape and size by migrating idealized plate triple junctions. The motion of the model triple junctions responds to the time‐dependent velocities of the adjacent plates. Each calculation includes four high‐viscosity plates in a 3 × 3 × 1 solution domain. We analyze the effect of plate evolution on the time dependence of plate velocity and heat flux in three different models characterized by lower mantle to upper mantle viscosity ratios of 30, 90, and 300. We examine the difference in behavior between calculations featuring fixed and mobile plate boundaries for each viscosity model. When plates are permitted to evolve in response to the convective vigor of the system, plate positions and shapes can change considerably while features in the high‐viscosity lower mantle may change very little. In addition, plate velocities and surface heat flux can be highly time dependent. We find that when the contrast between lower mantle and upper mantle viscosity magnitude is a factor of 30, surface velocities may fluctuate by 75% of the mean value and heat flux by 60%. We also find that plate velocity evolution is characterized by periods of reorganization, punctuating more stable periods. When the lower mantle viscosity is increased to 90 times the upper mantle value, plate reorganization events also occur, but only when plate boundary motion is enabled. When the lower mantle to upper mantle viscosity contrast is increased to a factor of 300, we find that the mean surface velocity and heat flux become very steady in cases both with and without plate boundary evolution, despite the substantial migration of convergent plate boundaries in the former case.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".