Canonical Extra Mixing in Low‐Mass Red Giants
Bibliographic record
Abstract
We have used the latest observational data on the evolutionary variations of the surface chemical composition in low-mass metal-poor stars, both in the field and in globular clusters, to constrain the basic properties of extra mixing in upper red giant branch (RGB) stars. Two different models of extra mixing have been incorporated into a stellar evolution code: a parametrical diffusion model and a model with rotation-induced turbulent diffusion. Application of the first model to the interpretation of the observed variations of the surface abundances of Li, C, and N and of the isotopic ratio 12C/13C in field stars has revealed that, for the majority of upper RGB stars, the depth and rate of extra mixing do not appear to vary appreciably from star to star. Furthermore, comparisons of our calculations with the results obtained by other authors show that at least the extra mixing depth does not seem to depend strongly on metallicity. Therefore, we propose to call this universal nonconvective mixing process "canonical extra mixing." We also put forward the hypothesis that some of the upper RGB stars may be experiencing "enhanced extra mixing," which is much faster (by a factor of ~100) and somewhat deeper than canonical extra mixing. This could explain the phenomenon of Li-rich giants. Enhanced extra mixing could also contribute to the O-Na and Mg-Al anticorrelations that are seen in some globular cluster red giants. A possible mechanism of extra mixing in upper RGB stars may be turbulent diffusion or/and meridional circulation induced by rotation. In this case, enhanced extra mixing requires rotational velocities that are ≈10 times as fast as those that are sufficient for the occurrence of canonical extra mixing. Observations do not exclude this possibility because (1) the dispersion in the surface rotational velocities of field Li-rich giants span a range of a factor of ~10 and (2) the extremely fast rotation of blue horizontal branch stars in globular clusters may require that their RGB precursors had been spun up appreciably by an external source. Star-to-star abundance variations in globular clusters may well have been produced as the result of both evolutionary and primordial processes. In the primordial scenario, the nuclearly processed material that is accreted by low-mass main-sequence stars may have originated primarily in earlier generations of massive asymptotic giant branch stars that had undergone hot bottom burning of their envelopes and partly in mass-losing upper RGB stars that had been just a bit more massive than the present-day main-sequence turnoff stars and had experienced extra mixing in the past.
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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 | 0.001 |
| 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".