The r-Java 2.0 code: nuclear physics
Bibliographic record
Abstract
Aims. We present r-Java 2.0, a nucleosynthesis code for open use that performs r-process calculations, along with a suite of other analysis tools. Methods. Equipped with a straightforward graphical user interface, r-Java 2.0 is capable of simulating nuclear statistical equilibrium (NSE), calculating r-process abundances for a wide range of input parameters and astrophysical environments, computing the mass fragmentation from neutron-induced fission and studying individual nucleosynthesis processes. Results. In this paper we discuss enhancements to this version of r-Java, especially the ability to solve the full reaction network. The sophisticated fission methodology incorporated in r-Java 2.0 that includes three fission channels (beta-delayed, neutron-induced, and spontaneous fission), along with computation of the mass fragmentation, is compared to the upper limit on mass fission approximation. The effects of including beta-delayed neutron emission on r-process yield is studied. The role of Coulomb interactions in NSE abundances is shown to be significant, supporting previous findings. A comparative analysis was undertaken during the development of r-Java 2.0 whereby we reproduced the results found in the literature from three other r-process codes. This code is capable of simulating the physical environment of the high-entropy wind around a proto-neutron star, the ejecta from a neutron star merger, or the relativistic ejecta from a quark nova. Likewise the users of r-Java 2.0 are given the freedom to define a custom environment. This software provides a platform for comparing proposed r-process sites.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.073 | 0.071 |
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".