WE‐E‐BRB‐03: Extracting Energy Fluence Distributions of X‐Rays Produced by Megavoltage Electron Beams Stopping in Thick Targets from Lateral Profiles Measured Using Ionization Chambers
Bibliographic record
Abstract
Purpose: To develop a method of extracting energy fluence distributions of x‐rays produced by megavoltage electron beams stopping in thick targets from lateral profiles measured using ionization chambers. Methods: High accuracy x‐ray lateral profiles measured using various ionization chambers with a set of build‐up caps for a number of target materials were recently reported. It was possible to extract energy fluence distributions from these profiles by using Monte Carlo calculations. The conversion factor was calculated as the ratio between the MC calculated energy fluence (using FLURZnrc) and the MC calculated absorbed dose in the air cavity of the ionization chamber (using “cavity”) at each position. The “measured” energy fluence lateral distribution was then obtained by multiplying the measured absorbed dose with these coefficients point by point, for each build‐up cap. The geometry, beam and material properties were simulated with BEAMnrc. The “cavity” user code was used to score the absorbed dose in the ionization chamber air cavity per incident electron up to angles of 25°. The energy fluence calculations were performed using the EGSnrc user code FLURZnrc. Results: The conversion factors for the PMMA build‐up caps were different by up to 40 % from those for the hevimet caps. However, when converting to energy fluence, for all targets the agreement between the energy fluence distributions obtained with each of the six build‐up caps was better than 1 %. The final energy fluence distribution for a particular target was obtained by averaging the distributions obtained for all six build‐up caps. Conclusions: The present study showed that it is possible to extract energy fluence distributions from x‐ray lateral in‐air profiles measured using commonly used Farmer type ionization chambers with various build‐up caps. The uncertainty on the distributions was better than 1 %. Partial support from NIH R01 CA104777‐01A2.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".