Bibliographic record
Abstract
Geant4 is a versatile Monte Carlo radiation transport simulation toolkit with a steep learning curve. This work introduces a user-code called M-TAG (Modular Radiation Teaching-Aid for Geant4), built on top of Geant4. M-TAG is designed to help gradually introduce the Geant4 toolkit to new users. The goal of Geant4 is to record quantities from the simulated radiation as it is transported through geometries. M-TAG simplifies the inclusion of new geometric elements and detector components in the simulation by including new classes. M-TAG also provides basic validated examples for some common detector development tasks. Geant4 intercom modules, called messenger classes, manage these classes. To validate M-TAG, simulations were performed to calculate the range of positrons in water. One hundred million decays at the center of a water-filled sphere with a radius of 1 m were allowed for fluorine-18, carbon-11, oxygen-15 and gallium-68. These results were compared to literature values. An inexperienced Geant4 user was tasked with creating a simulation model for a plastic scintillator-based detector and conducting basic tests to assess the effectiveness of M-TAG as a teaching tool. The simulation involved calculating the dose to the detector's sensitive volume using a 2x2 cm planar monoenergetic photon source spanning energies from 20 to 100 keV. One billion particles were simulated twice: once with the actual detector geometry and once with the sensitive volume replaced by water. The validity of M-TAG was also verified by computing dose ratios and comparing them with mass-attenuation ratios obtained from NIST XCOM data sets. The mean positron travel distances were within the distribution of literature values. Simulated positron energy spectra means were within 1.8% of literature means. Simulated dose ratios agreed with literature values within uncertainties. We have developed and verified a modular Geant4 teaching aid called M-TAG. It was used to introduce a new user to Geant4, who used it to perform further validation simulations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.018 |
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".