Po‐Poster ‐ 08: Comparison of two methods for commissioning BEAMnrc Elekta SL25 accelerator model
Bibliographic record
Abstract
Incident electron beam parameters of Monte Carlo linear accelerator model are typically determined by comparing computed profiles and depth‐dose curves with measurements. Several variations have been suggested in literature: Sheikh 2002 used kerma equivalent off‐axis factors while van der Zee 1999 used cross profiles in large water phantom. In this study, we compared both methods. The BEAMnrc and DOSXYZnrc codes are used to simulate 6MV and 25MV photon beam from medical linear accelerator Elekta SL25. The mean energy and radial width of the incident electron beam intensity distributions (assumed Gaussian and cylindrically symmetric) are derived by both in air and in water technique. Excluding region of electronic disequilibrium (build‐up and penumbra) local dose difference between simulated and measured curves must be under 2% to found agreement. The statistical uncertainties on calculated dose at each point is about 0.7% (or better) at 1 sigma level. We show that off‐axis curves are more sensitive to mean energy of electron beam than depth‐dose curves. The matching mean energy and radial parameters of the incident electrons beam (6MV & 25MV) for both in air and in water technique converge within 6%. Particular attention is also devoted to measurement in air and their consequences on matching parameters.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".