The Sensitivity of Core Heat Flux to the Modeling of Plate‐Like Surface Motion
Bibliographic record
Abstract
Abstract The generation of a magnetic field and the presence of tectonic plates are fundamental aspects of Earth's evolution. Viable dynamic models of terrestrial mantle convection therefore require the existence of heat transport from the core and a surface characterized by piecewise uniform surface velocity domains (i.e., plates). To test the compatibility of these two requirements, we varied energy input, rheology, and compositional heterogeneity in more than 70 mantle convection simulations. Calculations are performed in a spherical annulus geometry. By systematic investigation, we demonstrate that Earth‐like core heat flux can be obtained with self‐consistent model plates. We find that, given an initial condition where model parameters are chosen to ensure a mobile surface, the mobility is not strongly influenced when H is varied by up to a factor of three. Consequently, in spherical models with steady core temperatures and internal heating rates, the latter quantity can be used to regulate core heat flow to fit within the bounds inferred for terrestrial values. In contrast, core heat flow is strongly sensitive to model yield stress. We systematically vary thermal viscosity contrast, an intrinsic depth‐dependent viscosity and the depth‐dependence of a stress‐dependent rheology and analyze how each factor affects both surface velocities and core heat flux. Finally, we show that the addition of an intrinsically dense component comprising 5% or less of the mantle volume does not affect surface mobility or plateness, although it can have a profound impact on heat loss from the core.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".