Bibliographic record
Abstract
Our purpose in this study was to investigate whether the Monte Carlo simulation can accurately predict output factors in air. Secondary goals were to study the head scatter components and investigate the collimator exchange effect. The Monte Carlo code, BEAMnrc, was used in the study. Photon beams of 6 and 18 MV were from a Varian Clinac 2100EX accelerator and the measurements were performed using an ionization chamber in a mini-phantom. The Monte Carlo calculated in air output factors was within 1% of measured values. The simulation provided information of the origin and the magnitude of the collimator exchange effect. It was shown that the collimator backscatter to the beam monitor chamber played a significant role in the beam output factors. However the magnitude of the scattered dose contributions from the collimator at the isocenter is negligible. The maximum scattered dose contribution from the collimators was about 0.15% and 0.4% of the total dose at the isocenter for a 6 and 18 MV beam, respectively. The scattered dose contributions from the flattening filter at the isocenter were about 0.9-3% and 0.2-6% of the total dose for field sizes of 4x4 cm2-40x40 cm2 for the 6 and 18 MV beam, respectively. The study suggests that measurements of head scatter factors be done at large depth well beyond the depth of electron contamination. The insight information may have some implications for developing generalized empirical models to calculate the head scatter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".