Assessment of evaluated (n,d) energy-angle elastic scattering distributions using MCNP simulations of critical measurements and simplified calculation benchmarks
Bibliographic record
Abstract
Different evaluated (n,d) energy-angle elastic scattering distributions produce k-effective differences in MCNP5 simulations of critical experiments involving heavy water (D 2 O) of sufficient magnitude to suggest a need for new (n,d) scattering measurements and/or distributions derived from modern theoretical nuclear models, especially at neutron energies below a few MeV.The present work focuses on the small reactivity change of <1 mk that is observed in the MCNP5 D 2 O coolant-void-reactivity calculation bias for simulations of two pairs of critical experiments performed in the ZED-2 reactor at the Chalk River Laboratories when different nuclear data libraries are used for deuterium.The deuterium data libraries tested include ENDF/B-VII.0,ENDF/B-VI.4,JENDL-3.3 and a new evaluation, labelled Bonn-B, which is based on recent theoretical nuclear-model calculations.Comparison calculations were also performed for a simplified, two-region, spherical model having an inner, 250-cm radius, homogeneous sphere of UO 2 , without and with deuterium, and an outer 20-cm-thick deuterium reflector.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".