Poster — Thur Eve — 20: Fine Tuning the Source Parameters of a Varian 6 MV BEAMnrc Model through the Simulation of Small Jaw Collimated Photon Fields
Bibliographic record
Abstract
Purpose: The goal of this work was to finalize the electron source parameters of a Varian 6 MV BEAMnrc model through the simulation of jaw collimated small fields. Methods and Materials: For nominal field sizes of 0.5, 0.6, 0.7, 0.8, 0.9 and 1.0 cm profile data was acquired in water using a stereotactic field diode (SFD) and EBT2 film. To achieve a positional error below ± 0.01 cm a stepper motor driven linear actuator device was incorporated into the Wellhofer water tank. At electron energies of 6.0, 6.1 and 6.2 MeV the spatial distribution of the electrons incident on the target was modelled as a Gaussian. The FWHM was decreased in steps of 0.010 cm from 0.150 to 0.100 cm. DOSXYZnrc simulations were run and profile data extracted for comparison. Results: The SFD and EBT2 profile data were found to be in good agreement with small differences believed to be the result of non‐uniformities inherent to the film. In all cases the measured field sizes were found to be smaller than the nominal and the simulated field sizes adjusted accordingly. At a FWHM = 0.150 cm the penumbral width was much too broad and the smallest field widths wider than experiment. As the FWHM was decreased from 0.150 to 0.110 cm the fit became progressively better, yet worsened at a FWHM = 0.100 cm for all but the smallest field size. Conclusion: Small jaw collimated fields can be used to fine tune the electron source parameters used in a BEAMnrc linac model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".