Epp - A C++ EGSnrc user code for Monte Carlo simulation of radiation transport
Bibliographic record
Abstract
Easy particle propagation (Epp) is a Monte Carlo simulation EGSnrc user code that we have developed for dose calculation in a voxelized volume, and to generate images of an arbitrary geometry irradiated by a particle source. The dose calculation aspect is a reimplementation of the function of DOSXYZnrc with new features added and some restrictions removed. Epp is designed for x-ray application, but can be readily extended to trace other kinds of particles. Epp is based on the EGSnrc C++ class library (egspp) which makes modeling particle sources and simulation geometries simpler than in DOSXYZnrc and other BEAM user codes based on EGSnrc code system. With Epp geometries can be modeled analytically or voxelized geometries, such as those in DOSXYZnrc, can be used. Compared to DOSXYZnrc (slightly modified from the official version for saving phase space information of photons leaving the geometry), Epp is at least two times faster. Photon propagation to the image plane is integrated into Epp (other particles possible with minor extension to the current code) with an ideal detector defined. When only the resultant images are needed, there is no need to save the particle data. This results in significant savings of data storage space, network load, and time for file I/O. Epp was validated against DOSXYZnrc for imaging and dose calculation by comparing simulation results with the same input. Epp can be used as a Monte Carlo simulation tool for faster imaging and radiation dose applications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.021 |
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".