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 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".