Incorporation of a combinatorial geometry package and improved scoring capabilities in the <scp>EGS</scp>nrc Monte Carlo Code system
Bibliographic record
Abstract
A description is given of a generic EGSnrc Monte Carlo user code, GenUC, which was developed as an attempt to simplify and optimize the geometry and scoring coding of EGSnrc user codes. GenUC was developed using the methodology of combinatorial geometry that allows a straightforward implementation of complicated geometric setups with intersecting boundaries, where subsequent modifications to the geometry are easily performed. Presently, GenUC has five elemental volumes that can be defined in any position in space: spheres, ellipsoids, parallelepipeds, and circular cylinders and cones. The mortran macro-based implementation of the combinatorial geometry package allows an easy definition/extension of any other elemental volume, e.g., elliptical cylinders and cones. The scoring of the relevant parameters and the output of the results in GenUC are performed with two CERN data analysis packages, which permit the generation of nonplanar phase space distribution files and can also be used for geometry verification, among many other capabilities. GenUC has been successfully applied to complex geometric setups, e.g., intracavitary brachytherapy applicators and was also benchmarked against the EGSnrc user code, DOSRZnrc.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".