Characterization of the water-equivalent material WTe for use in electron beam dosimetry
Bibliographic record
Abstract
This paper describes the characterization of the water-equivalent material WTe (produced by St Bartholomew's Hospital, London). The use of epoxy resin phantoms offers a number of advantages over water for radiotherapy dosimetry in terms of robustness and ease of use, but the published uncertainties in the fluence corrections for such phantoms significantly increase the overall uncertainty in the measurement of absorbed dose to water at the reference point. Depth-ionization data were obtained in water and WTe for electron beams in the range 4 MeV to 16 MeV and it was found that the measured fluence in the WTe phantom was approximately 0.4% higher than in a water phantom at the same depth. For measurements only at the reference depth this difference was less, with the fluence in the WTe phantom being 0.2% higher. The standard uncertainty on this value is estimated to be +/- 0.12%, which represents a significant improvement over previous measurements. It was also found that the range scaling factor is not equal to unity, as previously recommended for this material, but that the data was best fitted by the relation 1 mm WTe = 1.01 mm water (with an uncertainty of +/- 0.2%). The results obtained confirm previous investigations of WTe as to its suitability for reference ion chamber dosimetry in the radiotherapy clinic. However, the recommendation is still to use a water phantom wherever possible.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".