Mobile or not mobile: exploring the linkage between deep mantle composition and early Earth surface mobility
Bibliographic record
Abstract
Numerical models of mantle convection help our understanding of the complex feedback between the plates and deep interior dynamics through space and time. Did the early Earth have plate tectonics, a stagnant lid, or something in between? The surface dynamics of the early Earth remain poorly understood. Current numerical models of mantle convection are constrained by present-day observations, but the behavior of the hotter, early Earth prior to the onset of plate tectonics is less certain. The early Earth may have possessed a large hot magma ocean trapped near the core-mantle boundary after formation during differentiation, and likely containing different elements from the surrounding mantle. We examine how composition-dependent properties in the deep mantle affect convection dynamics and surface mobility in high Rayleigh number models featuring plastic yielding. Our Newtonian models indicate that increased conductivity or decreased viscosity flattens basal topography while also increasing the potential for surface yielding. We vary the viscosity, thermal conductivity, and internal heating in a compositionally distinct basal magma ocean and explore the compositional topography, insulation effects and surface stresses for non-Newtonian rheology. Models are run using a variety of crustal compositions, such as the inclusion of primordial continental material before the onset of plate tectonics. We monitor the surface for plate-like behavior. Since convective vigour is very strong in the early Earth, specialized tracer methods are employed for increased accuracy. In our models, Stokes flow solutions are obtained using a multigrid method specifically designed to handle large viscosity contrasts and non-Newtonian rheology.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".