Measurements and Monte Carlo simulations for reference dosimetry of external radiation therapy beams
Bibliographic record
Abstract
About 50 % of cancer patients receive some form of radiation therapy over the course of treatment.The accuracy of external beam radiation therapy relies on careful reference dosimetry, the calibration of treatment machine output.A comprehensive investigation of reference dosimetry of photon and electron beams is presented using measurements and Monte Carlo simulations, with specific focus on the determination of accurate beam quality conversion factors, k Q , which are needed to convert the reading of an ionization chamber calibrated in a cobalt-60 reference field to the absorbed dose to water in a clinical beam.Measurements of k Q factors for plane-parallel chambers are determined as the ratio of absorbed dose calibration coefficients, traceable to the Canadian primary standard water calorimeter, in a cobalt-60 beam to those from linac photon beams.The poor repeatability of these measurements indicates that I am greatly indebted to my supervisor and mentor, Dave Rogers, for all he has done for me over the course of this degree.Through his hard work, his unvarying availability to discuss even the most minor aspect of research and his attention to detail, he is the epitome of the physicist I aspire to be and a constant source of inspiration.I also owe a great deal of gratitude to Malcolm McEwen, with whom many hours were spent in the linac bunker at NRC where he taught me through his meticulousness with measurements, insightfulness and patience.The confidence Malcolm showed in my abilities meant a lot to me.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".