Toward a Unified Model for the Thermal State of the Planetary Mantle: Estimations From Mean Field Deep Learning
Bibliographic record
Abstract
Abstract The cooling of terrestrial planets depends upon the mechanism whereby heat is transferred from the interior to the surface and thereafter is radiated to the space. The surface boundary condition, geometry, internal processes, and the Rayleigh number are the most important controlling parameter influencing the rate of cooling. In this study we employ machine learning algorithms to train learning models that estimate the thermal state of the planets based on their curvature ( f = r cmb / r surf ), Rayleigh number, and internal heating for two end member planets—rigid and free‐slip surface planets. Three‐dimension‐spherical control volume models are used to generate training samples. Employing regression learning algorithms, we show that supervised machine learning (SML) techniques can successfully predict the thermal state of the simplified model planets (predicted results versus calculated) with the possibility of extending the method to the actual planets where the complexities are incorporated into the model. The predictive models can be used in estimation of the surface heat flux and the planets' mean temperature. We find that deep learned models provide higher prediction accuracies than those obtained from simple machine leaning models with polynomialized features. The prediction accuracies in deep learned models for the unseen data approached 99% for both mean mantle temperature and mean surface heat flux. As such, deep learning techniques can be employed in more complex mantle problems in which more complex and highly pressure and temperature dependent processes are present.
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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 | 0.000 | 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".