Investigation of field output factors using IAEA-AAPM TRS-483 code of practice recommendations and Monte Carlo simulation for 6 MV photon beams
Bibliographic record
Abstract
Abstract Introduction: This study aims to experimentally determine field output factors using the methodologies suggested by the IAEA-AAPM TRS-483 for small field dosimetry and compare with the calculation from Monte Carlo (MC) simulation. Methods: The IBA-CC01, Sun Nuclear EDGE and IBA-SFD detectors were employed to determine the uncorrected and the corrected field output factors for 6 MV photon beams. Measurements were performed at 100 cm source to axis distance, 10 cm depth in water, and the field sizes ranged from 1 × 1 to 10 × 10 cm2. The use of field output correction factors proposed by the TRS-483 was utilised to determine field output factors. The measured field output factors were compared to that calculated using the egs_chamber user code. Results: The decrease in the percentage standard deviation of the measured three detectors was observed after applying the field output correction factors. Measured field output factors using CC01 and EDGE detectors agreed with MC values within 3% for field sizes down to 1 × 1 cm2, except the SFD detector. Conclusions: The corrected field output factors agree with the calculation from MC, except the SFD detector. CC01 and EDGE are suitable for determining field output factors, while the SFD may need more implementation of the intermediate field method.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".