i process and CEMP-s+r stars
Bibliographic record
Abstract
We investigate whether the anomalous elemental abundance patterns in some of the C-enhanced metal-poor-r/s (CEMP-r/s) stars are consistent with predictions of nucleosynthesis yields from the i-process, a neutron-capture regime at neutron densities intermediate between those typical for the slow (s) and rapid (r) processes.Conditions necessary for the i-process are expected to be met at multiple stellar sites, such as the He-core and He-shell flashes in low-metallicity low-mass stars, super-AGB and post-AGB stars, as well as low-metallicity massive stars.We have found that single-exposure one-zone simulations of the i-process reproduce the abundance patterns in some of the CEMP-r/s stars much better than the model that assumes a superposition of yields from sand r-process sources.Our previous study of nuclear data uncertainties relevant to the i-process revealed that they could have a significant impact on the i-process yields obtained in our idealized one-zone calculations, leading, for example, to ∼ 0.7dex uncertainty in our predicted [Ba/La] ratio.Recent 3D hydrodynamic simulations of convection driven by a He-shell flash in post-AGB Sakurai's object have discovered a new mode of non-radial instabilities: the Global Oscillation of Shell H-ingestion.This has demonstrated that spherically symmetric stellar evolution simulations cannot be used to accurately model physical conditions for the i-process.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".