<i>r</i>-Java: an<i>r</i>-process code and graphical user interface for heavy-element nucleosynthesis
Bibliographic record
Abstract
We present r-Java, an r-process code for open use that performs r-process nucleosynthesis calculations. Equipped with a simple graphical user interface, r-Java is capable of carrying out nuclear statistical equilibrium (NSE), as well as static and dynamic r-process calculations, for a wide range of input parameters. In this introductory paper, we present the motivation and details behind r-Java and results from our static and dynamic simulations. Static simulations are explored for a range of neutron irradiation and temperatures. Dynamic simulations are studied with a parameterized expansion formula. Our code generates the resulting abundance pattern based on a general entropy expression that can be applied to both degenerate and non-degenerate matter, allowing us to track the rapid density and temperature evolution of the ejecta during the initial stages of ejecta expansion. At present, our calculations are limited to the waiting-point approximation. We encourage the nuclear astrophysics community to provide feedback on the code and related documentation, which is available for download from the website of the Quark-Nova Project: http://quarknova.ucalgary.ca/.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.084 | 0.070 |
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".