PyNjoy2016: an Open Source System for Producing Cross Sections Libraries for DRAGON5 and SERPENT2
Bibliographic record
Abstract
PyNjoy2016 was born from the fusion of PyNjoy2012 and NJOY2016. This new software is dedicated to the production of libraries for DRAGON5 and SERPENT2, in DRAGLIB and ACE formats, respectively, for deterministic and Monte-Carlo calculations, from a nuclear data evaluation in ENDF-6 format. Among the various improvements over its predecessor, PyNjoy2016 supports the advanced energy deposition model of SERPENT2. It also includes CPU-parallelized features to facilitate the propagation of uncertainties by Total Monte-Carlo method. For that, it relies either on SANDY code, called on-the-fly, or on presampled random TENDL files. Moreover, it allows consistent production of ACE and DRAGLIB files, deriving from the same NJOY computations. The exact deterministic bias of a specific DRAGON5 calculation scheme can therefore be calculated. In contrast, the usual comparisons between deterministic and Monte-Carlo methods are often not based on consistently produced libraries, leading to an error in the calculated deterministic bias. Finally, its fully open source nature (BSD license) ensures a wider accessibility than previous versions of PyNjoy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.145 | 0.069 |
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".