Bibliographic record
Abstract
Abstract Background Water phantoms are required to perform reference dosimetry and beam quality measurements but there are no published studies about the size requirements for such phantoms. Purpose To investigate, using Monte Carlo techniques, the size requirements for water phantoms used in reference dosimetry and/or to measure the beam quality specifiers and . Methods The EGSnrc application DOSXYZnrc is used to calculate , the dose per incident fluence at 10 cm depth in a water phantom irradiated by incident beams of or 6 MV photons. The water phantom dimensions are varied from to and occasionally smaller. The and values are also calculated with care being taken to distinguish results when using Method A (changing depth of water in phantom) and Method B (moving entire phantom). Typical statistical uncertainties are 0.03%. Results Phantom dimensions have only minor effects for phantoms larger than . A table of corrections to the dose at 10 cm depth in beams of or 6 MV photons are provided and range from no correction to 0.75% for a beam incident on a phantom. There can be distinct differences in the values measured using Method A or Method B, especially for smaller phantoms. It is explicitly demonstrated that, within 0.15%, values for a phantom measured using Method A or B are independent of source detector distance between 40 and 200 cm. Conclusions The phantom sizes recommended in the TG‐51 and IAEA TRS‐398 reference dosimetry protocols are adequate for accurate reference dosimetry and in some cases are even conservative. Correction factors are necessary for accurate measurement of the dose at 10 cm depth in smaller phantoms and these factors are provided. Very accurate beam quality specifiers are not required for reference dosimetry itself, but for specifying beam stability and characteristics it is important to specify phantom sizes and also the method used for measurements.
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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 | 0.008 | 0.026 |
| 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) | 0.013 | 0.003 |
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".