Thermohaline Mixing: Does It Really Govern the Atmospheric Chemical Composition of Low-Mass Red Giants?
Bibliographic record
Abstract
First results of our 3D numerical simulations of thermohaline convection driven by 3He burning in a low-mass RGB star at the bump luminosity are presented. They confirm our previous conclusion that this convection has a mixing rate which is a factor of 50 lower than the observationally constrained rate of RGB extra-mixing. It is also shown that the large-scale instabilities of salt-fingering mean field (those of the Boussinesq and advection-diffusion equations averaged over length and time scales of many salt fingers), which have been observed to increase the rate of oceanic thermohaline mixing up to one order of magnitude, do not enhance the RGB thermohaline mixing. We speculate on possible alternative solutions of the problem of RGB extra-mixing, among which the most promising one that is related to thermohaline mixing is going to take advantage of the shifting of salt-finger spectrum towards larger diameters by toroidal magnetic field.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".