Multi-group cross section library generation for HTGR lattice physics code XPZ
Bibliographic record
Abstract
Abstract The XPZ code was previously developed for the lattice physics computation in High Temperature Gas-cooled Reactors (HTGRs), which adopted the multi-group cross section library converted from the existing open-source DRAGON library. In this paper, a new format of multi-group cross section library named XPZLIB has been implemented in XPZ code. XPZLIB is designed in binary and HDF5 formats, including detailed data contents for resonance, transport and depletion calculations. A new data-processing module named XPZR is developed based on NJOY-2016 to generate nuclide dependent XPZLIB from the most recent evaluated nuclear data, and besides, the PyNjoy-2016 system is developed for automatic generation of integrated XPZLIB including a complete set of nuclides. The new generated XPZLIB is presented with the XPZ code. Numerical results demonstrate the accuracy of the new library XPZLIB and the reliability of the data processing scheme. Moreover, the influence of different versions of ENDF/B data is investigated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".