Evaluation of MCNP5 and EGS4 for the simulation of <i>in vivo</i> strontium XRF measurements
Bibliographic record
Abstract
Abstract In order to optimize x‐ray fluorescence (XRF) experiments, Monte Carlo (MC) simulations provide a valuable tool that allows different system setups and experimental conditions to be explored in a controlled way. Electron gamma shower (EGS4) and Monte Carlo N‐Particle5 (MCNP5) are two widely used MC programs that simulate the photon and electron transport in great detail and allow for the simulation of an arbitrary experimental geometry. Tested in 2001 by Al‐Ghorabie et al. , earlier versions of these programs failed in reproducing the outcome of a platinum XRF in vivo measurement. The authors found that the discrepancy between measured and simulated results could be attributed to the free electron approximation made by the programs in simulating the Compton scattering. More recent versions of EGS4 and MCNP have updated their treatment for photon transport at low energies and have included Doppler broadening in the Compton profile. In this study we test the capability of these new versions of EGS4 and MCNP in reproducing the outcome of an in vivo strontium XRF measurement. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.010 |
| 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 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".