Comparative evaluation of modern dosimetry techniques near low‐ and high‐density heterogeneities
Bibliographic record
Abstract
The purpose of this study is to compare performance of several dosimetric meth-ods in heterogeneous phantoms irradiated by 6 and 18 MV beams. Monte Carlo (MC) calculations were used, along with two versions of Acuros XB, anisotropic analytical algorithm (AAA), EBT2 film, and MOSkin dosimeters. Percent depth doses (PDD) were calculated and measured in three heterogeneous phantoms. The first two phantoms were a 30 × 30 × 30 cm3 solid-water slab that had an air-gap of 20× 2.5 × 2.35 cm3. The third phantom consisted of 30 × 30 × 5 cm3 solid water slabs, two 30 × 30 × 5 cm3 slabs of lung, and one 30 × 30 × 1 cm3 solid water slab. Acuros XB, AAA, and MC calculations were within 1% in the regions with particle equilibrium. At media interfaces and buildup regions, differences between Acuros XB and MC were in the range of +4.4% to -12.8%. MOSkin and EBT2 measurements agreed to MC calculations within ~ 2.5%, except for the first cen-timeter of buildup where differences of 4.5% were observed. AAA did not predict the backscatter dose from the high-density heterogeneity. For the third, multilayer lung phantom, 6 MV beam PDDs calculated by all TPS algorithms were within 2% of MC. 18 MV PDDs calculated by two versions of Acuros XB and AAA differed from MC by up to 2.8%, 3.2%, and 6.8%, respectively. MOSkin and EBT2 each differed from MC by up to 2.9% and 2.5% for the 6 MV, and by -3.1% and ~2% for the 18 MV beams. All dosimetric techniques, except AAA, agreed within 3% in the regions with particle equilibrium. Differences between the dosimetric techniques were larger for the 18 MV than the 6 MV beam. MOSkin and EBT2 measurements were in a better agreement with MC than Acuros XB calculations at the interfaces, and they were in a better agreement to each other than to MC. The latter is due to their thinner detection layers compared to MC voxel sizes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".