Bibliographic record
Abstract
Objectives of this chapter Mantle convection involves many processes and physical effects that are seldom found in other convective systems. We evaluate the most important ones and discuss the impact of each one on the heat loss properties and thermal structure of the mantle. We developscalings for the heat flux and typical velocity and demonstrate that they can all be reduced to simple statements on the dynamics of a thermal boundary layer. We aim at an understanding of each process and its control variables, and do not attempt to build an all-encompassing physical model of mantle convection. Introduction Compared to Rayleigh–Benard convection that has been studied in Chapter 5, convection in the Earth's mantle involves a series of processes that all act to enhance the impact of the upper thermal boundary layer on heat transport. The surface heat flux evacuates heat released by radioactive decay within the mantle as well as sensible heat due to secular cooling. The presence of continents over part of the Earth's surface restricts the efficiency of heat loss to the atmosphere and hydrosphere, and enhances heat flux through oceanic areas. Separation of the oceanic and continental domains with different heat transport characteristics at the Earth's surface generates large-scale horizontal temperature variations in the shallow mantle. Mantle rheology is highly sensitive to temperature, implying that cold material in the upper thermal boundary layer deforms less readily than the interior.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".