Comparison of measured and Monte Carlo calculated dose distributions in inhomogeneous phantoms in clinical electron beams
Bibliographic record
Abstract
Calculations of dose distributions in heterogeneous phantoms in clinical electron beams, carried out using the fast voxel Monte Carlo (MC) system XVMC and the conventional MC code EGSnrc, were compared with measurements. Irradiations were performed using the 9 MeV and 15 MeV beams from a Varian Clinac-18 accelerator with a 10 x 10 cm2 applicator and an SSD of 100 cm. Depth doses were measured with thermoluminescent dosimetry techniques (TLD 700) in phantoms consisting of slabs of Solid Water (SW) and bone and slabs of SW and lung tissue-equivalent materials. Lateral profiles in water were measured using an electron diode at different depths behind one and two immersed aluminium rods. The accelerator was modelled using the EGS4/BEAM system and optimized phase-space files were used as input to the EGSnrc and the XVMC calculations. Also, for the XVMC, an experiment-based beam model was used. All measurements were corrected by the EGSnrc-calculated stopping power ratios. Overall, there is excellent agreement between the corrected experimental and the two MC dose distributions. Small remaining discrepancies may be due to the non-equivalence between physical and simulated tissue-equivalent materials and to detector fluence perturbation effect correction factors that were calculated for the 9 MeV beam at selected depths in the heterogeneous phantoms.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".