Poster ‐ 20: Detector selection for commissioning of a Monte Carlo based electron dose calculation algorithm
Bibliographic record
Abstract
Objective: Commissioning of a Monte Carlo based electron dose calculation algorithm requires percentage depth doses (PDDs) and beam profiles which can be measured with multiple detectors. Electron dosimetry is commonly performed with cylindrical chambers but parallel plate chambers and diodes can also be used. The purpose of this study was to determine the most appropriate detector to perform the commissioning measurements. Methods: PDDs and beam profiles were measured for beams with energies ranging from 6 MeV to 15 MeV and field sizes ranging from 6 cm × 6 cm to 40 cm × 40 cm. Detectors used included diodes, cylindrical and parallel plate ionization chambers. Beam profiles were measured in water (100 cm source to surface distance) and in air (95 cm source to detector distance). Results: PDDs for the cylindrical chambers were shallower (1.3 mm averaged over all energies and field sizes) than those measured with the parallel plate chambers and diodes. Surface doses measured with the diode and cylindrical chamber were on average larger by 1.6 % and 3% respectively than those of the parallel plate chamber. Profiles measured with a diode resulted in penumbra values smaller than those measured with the cylindrical chamber by 2 mm. Conclusion: The diode was selected as the most appropriate detector since PDDs agreed with those measured with parallel plate chambers (typically recommended for low energies) and results in sharper profiles. Unlike ion chambers, no corrections are needed to measure PDDs, making it more convenient to use.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".