Monte Carlo calculation of photo-neutron dose produced by circular cones at 18 MV photon beams
Bibliographic record
Abstract
The aim of this study is to calculate neutron contamination at the presence of circular cones irradiating by 18 MV photons using Monte Carlo code. Small photon fields are one of the most useful methods in radiotherapy. One of the techniques for shaping small photon beams is applying circular cones made of lead. Using this method in high energy photon due to neutron contamination is a crucial issue. Initially, Varian linac producing 18 MV photons was simulated and after validating the code, various circular cones were also simulated. Then, the number of neutrons, neutron equivalent dose and absorbed dose per Gy of photon dose were calculated along the central axis. Number of neutrons per Gy of photon dose had their maximum value at depth of 2 cm and these values for 5, 10, 15, 20 and 30 mm circular cones were 9.02, 7.76, 7.61, 6.02 and 5.08 (n cm−2 Gy−1), respectively. Neutron equivalent doses per Gy of photon dose had their maximum at the surface of the phantom and these values for mentioned collimators were 1.48, 1.33, 1.31, 1.12 and 1.08 (mSv Gy−1), respectively. Neutron absorbed doses had their maximum at the surface of the phantom and these values for mentioned collimators sizes were 103.74, 99.71, 95.77, 81.46 and 78.20 (μGy/Gy), respectively. As the field size gets smaller, number of neutrons, equivalent and absorbed dose per Gy of photon increase. Also, neutron equivalent dose and absorbed dose are maximum at the surface of phantom and then these values will be decreased.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".