Making the EGSnrc/BEAMnrc system more efficient, accurate and realistic in simulating kilovoltage x-ray systems
Bibliographic record
Abstract
Canada de reproduire, publier, archiver, sauvegarder, conserver, transmettre au public par telecommunication ou par I'lnternet, preter, distribuer et vendre des theses partout dans le monde, a des fins commerciales ou autres, sur support microforme, papier, electronique et/ou autres formats.R ep ro d u ced with p erm ission o f th e copyright ow ner.Further reproduction prohibited w ithout perm ission.S ta te m e n t o f o rig in a lity T he contents of this thesis is a sum m ary of th e a u th o r's work during the course of his M.Sc.program at C arleton University.T he contents have been published in, or subm itted to, various scientific journals.P a rts of th e contents have also been presented a t national and international conferences.T he following is a sum m ary of th e literature upon which this thesis is based.Except for item 7 in th e list below, th e author of this thesis perform ed all the calculations and drafted and edited all th e m anuscripts. P eer-review ed full papers1. E. S. M. Ali and D. W. O. Rogers, Efficiency improvements of x-ray simulations in EGSnrc user-codes using B rem sstrahlung Cross Section Enhancem ent (BCSE), Med.Phys.34, 2143 -2154, (2007).1 2. E. S. M. Ali and D. W. O. Rogers, Benchm arking EG Snrc in th e kilovoltage range against experim ental m easurem ents of charged particle backscatter coeffi cient, Phys.Med.Biol., to be subm itted (Septem ber 2007).2 3. E. S. M. Ali and D. W. O. Rogers, Energy spectra and angular distribution of charged particles backscattered from solid targets, J. Phys.D:
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".