Poster ‐ 23: Dosimetric Characterization and Transferability of an Accessory Mounted Mini‐Beam Collimator
Bibliographic record
Abstract
Objective: The dosimetric characterization of an accessory‐mounted mini‐beam collimator across three beam matched linear accelerators. Materials and Methods: Percent depth dose and profiles were measured for the open and mini‐beam collimated fields. The average beam quality and peak‐to‐valley dose ratio (PVDR), the ratio of average peak dose to average valley dose, were obtained from these measurements. The open field relative output and the mini‐beam collimator factor, the ratio of the mini‐beam dose to open field dose at the beam center, were measured for square fields of side 2, 3, 4, and 5 cm. Mini‐beam output as a function of collimator inclination angle relative to the central axis was also investigated. Results and Discussion: Beam quality for both the open and mini‐beam collimated fields agreed across all linacs to within ±1.0%. The PVDR was found to vary by up to ±6.6% from the mean. For the 2, 3, and 4 cm fields the average open field relative output with respect to the 5 cm field was 0.874±0.4%, 0.921±0.3%, and 0.962±0.1%. The average collimator factors were 0.450±3.9%, 0.443±3.9%, 0.438±3.9%, and 0.434±3.9%. A decrease in collimator factor greater than 7% was found for an inclination angle change of 0.09°. Conclusion: The mini‐beam collimator has revealed a difference between the three linacs not apparent in the open field data, yet transferability can still be attained through thorough dosimetric characterization.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.002 |
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".