A two step algorithm for predicting portal dose images in arbitrary detectors
Bibliographic record
Abstract
An algorithm has been presented which accurately predicts portal dose images for arbitrary detectors and air gaps. Implementation involves first predicting the primary and scattered photon fluence into a detector, then predicting the dose response of the detector. The algorithm utilizes pre-calculated libraries of scatter fluence kernels and dose deposition kernels, which are obtained through Monte Carlo radiation transport techniques. The algorithm is fast, allows a separation of primary and scatter, and can model arbitrary detector materials. The accuracy of the algorithm was investigated for a 6 MV beam over air gaps of 10-80 cm for a PMMA slab phantom, a PMMA slab with a cork inhomogeneity, and an anthropomorphic phantom. Two different detector configurations were used, involving low and high atomic number buildup material. In most cases (>95%), the difference between predicted and measured doses is within 3%, and penumbra's are within 4 mm. This level of accuracy is within the guidelines set out for treatment planning dose calculation algorithms. It is concluded that this approach represents a fast, accurate, and flexible solution to portal dose image prediction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".