Development and validation of a BEAMnrc component module for a miniature multileaf collimator
Bibliographic record
Abstract
A new component module (CM) named mini multileaf collimator (mMLC) was developed for the Monte Carlo code BEAMnrc. It models the geometry of the add-on miniature multileaf collimator ModuLeaf (MRC Systems GmbH, Heidelberg, Germany, now part of Siemens, Erlangen, Germany). The new CM is partly based on the existing CM called DYNVMLC. The development was performed using a modified EGSnrc platform which enables us to work in the Microsoft Visual Studio environment. In order to validate the new CM, the PRIMUS linac with 6 MV x-rays (Siemens OCS, Concord, CA, USA) equipped with the ModuLeaf mMLC was modelled. Validation was performed by two methods: (a) a ray-tracing method to check the correct geometry of the multileaf collimator (MLC) and (b) a comparison of calculated and measured results of the following dosimetrical parameters: output factors, dose profiles, field edge position penumbra, MLC interleaf leakage and transmission values. Excellent agreement was found for all parameters. It was, in particular, found that the relationship between leaf position and field edge depending on the shape of the leaf ends can be investigated with a higher accuracy by this new CM than by measurements demonstrating the usefulness of the new CM.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".