Technical Note: Taking EGSnrc to new lows: Development of egs++ lattice geometry and testing with microscopic geometries
Bibliographic record
Abstract
PURPOSE: This work introduces a new lattice geometry library, egs_lattice, into the EGSnrc Monte Carlo code, which can be used for both modeling very large (previously unfeasible) quantities of geometries (e.g., cells or gold nanoparticles (GNPs)) and establishing recursive boundary conditions. The reliability of egs_lattice, as well as EGSnrc in general, is cross-validated and tested at short length scales and low energies. METHODS: New Bravais, cubic, and hexagonal lattice geometries are defined in egs_lattice and their transport algorithms are described. Simulations of cells and GNP-containing cavities are implemented to compare to independent, published Geant4-DNA and PENELOPE results. Recursive boundary conditions, implemented through a cubic lattice, are used to perform electron Fano cavity tests. The Fano test is performed on three different-sized cells containing GNPs in the region around the nucleus for three source energies. RESULTS: Lattices are successfully implemented in EGSnrc, and are used for validation. EGSnrc calculated the dose to cell cytoplasm and nucleus when irradiated by an internal electron source with a median difference of 0.6% compared to published Geant4-DNA results. EGSnrc calculated the ratio of dose to a microscopic cavity containing GNPs over dose to a cavity containing a homogeneous mixture of gold, and results generally agree (within 1%) with published PENELOPE results. The electron Fano cavity test is passed for all energies and cells considered, with sub-0.1% discrepancies between EGSnrc-calculated and expected values. Additionally, the recursive boundary conditions used for the Fano test provided a factor of over a million increase in efficiency in some cases. CONCLUSIONS: The egs_lattice geometry library, currently available as a pull request on the EGSnrc GitHub "develop" branch, is now freely accessible as open-source code. Lattice geometry implementations cross-validated with independent simulations in other MC codes and verified with the electron Fano cavity test demonstrate not only the reliability of egs_lattice, but also, by extension, EGSnrc's ability to simulate transport in nanometer geometries and score in microscopic cavities.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".