Enhanced Extra Mixing in Low‐Mass Red Giants: Lithium Production and Thermal Stability
Bibliographic record
Abstract
We show that canonical extra mixing with a diffusion coefficient D mix ≈ 10 9 cm 2 s -1 , which is thought to start working in the majority of low-mass stars when they reach the bump luminosities on the red giant branch (RGB), cannot lead to an Li flash or a thermal instability, as has been proposed. The abundance levels of 7 Li measured in the most extreme Li-rich giants can be reproduced with models including enhanced extra mixing with a diffusion coefficient D mix ≈ 10 11 cm 2 s -1 . We propose that if extra mixing in RGB stars is driven by rotation, then enhanced extra mixing and Li enrichment in some of these stars can be triggered by their spinning up by an external source of angular momentum. As plausible mechanisms of the spinning up, we consider tidal synchronization of a red giant's spin and orbital rotation in a close binary system and engulfment of a massive planet. The most convincing theoretical argument in favor of our hypothesis is a finding that a 10-fold increase of the spin angular velocity of a solar metallicity upper RGB star results in appropriate changes of both extra-mixing depth and rate, exactly as required for efficient Li production. We regard the existence of binary and single RGB stars with rotational velocities approaching ~10% of their equatorial Keplerian velocities, as well as the much larger proportion of Li-rich giants (~50%) among rapidly rotating objects, as the observational support for our hypothesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".