Sci-Thur AM: Planning - 01: Experimental and Monte Carlo verification of Acuros XB calculations near low and high density heterogeneities
Bibliographic record
Abstract
The purpose of this study was to examine the accuracy of AcurosXB and AAA algorithms near low and high density heterogeneities of different densities using EBT2 film, MOSFET detector “MOSkin” and Monte Carlo calculations using BEAMnrc/DOSXYZnrc. Three different interfaces were used that included a solid water phantom with 2×2×30cm3 rectangular air gap, rectangular steel insert, and a slab of water embedded between two slabs of lung material. 6MV photon beam with field size of 10×10cm2 was used for the first two geometries and a 3×3cm2-field was used for the third. Percentage Depth Doses were measured and calculated at the beam central axis. Calculation voxel of 0.1×0.1×0.1cm3 was used by all three algorithms. For all configurations, AcurosXB and AAA agreed to within ±1.3% with MC before the inhomogeneity. The PDD measurements using MOSkin and EBT2 in water, apart from 0.2cm layer near heterogeneity, agreed with the MC within ±2.2%. Within 0.1cm before the water-air interface AcurosXB and AAA overestimated the dose by 4.7% and 1.6%, respectively. Whereas, in the 0.1cm beyond the air-water interface, AcurosXB and AAA overestimated the dose by 2.4% and 16.2% respectively. In the 0.1cm before the water-steel interface, AcurosXB overestimated the dose by 4.7% and AAA underestimated the dose by 9.5%; beyond the steel-water interface AcurosXB and AAA overestimated the dose by 3.6% and 7.7% respectively. For the lung phantom configuration, AcurosXB and AAA were in agreement with MC within 2% throughout the phantom. These results demonstrate improved performance of AcurosXB as compared to AAA in considered conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".