Validation of<scp>GEANT4</scp>, an object-oriented Monte Carlo toolkit, for simulations in medical physics
Bibliographic record
Abstract
GEANT4 (GEometry ANd Tracking 4) is an object-oriented Monte Carlo simulation toolkit that has been developed by a worldwide collaboration of scientists. It simulates the passage of particles through matter. In order to validate GEANT4 for medical physics applications, different simulations are conducted. The results are compared to published results based on three Monte Carlo codes widely used in medical physics: MCNP, EGS4, and EGSnrc. When possible, the simulation results are also compared to experimental data. Different geometries are tested (multilayer and homogeneous phantoms), different sources considered (point-source and broad parallel beam), and different primary particles simulated (photons and electrons) at different energies. For the heterogeneous media, there are notable differences between the Monte Carlo codes reaching up to over 5% in relative difference. For the monoenergetic electrons in a homogeneous medium, the difference between GEANT4 and the experimental measurements is similar to the difference between EGSnrc and the experimental measurements; for the depth-dose curves, the difference expressed as a fraction of the peak dose is always smaller than 4%. We conclude that GEANT4 is a promising Monte Carlo simulation toolkit for low-energy medical 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".