Evaluation of the water-equivalence of plastic materials in low- and high-energy clinical proton beams
Bibliographic record
Abstract
The aim of this work was to evaluate the water-equivalence of new trial plastics designed specifically for light-ion beam dosimetry as well as commercially available plastics in clinical proton beams. The water-equivalence of materials was tested by computing a plastic-to-water conversion factor, [Formula: see text]. Trial materials were characterized experimentally in 60 MeV and 226 MeV un-modulated proton beams and the results were compared with Monte Carlo simulations using the FLUKA code. For the high-energy beam, a comparison between the trial plastics and various commercial plastics was also performed using FLUKA and Geant4 Monte Carlo codes. Experimental information was obtained from laterally integrated depth-dose ionization chamber measurements in water, with and without plastic slabs with variable thicknesses in front of the water phantom. Fluence correction factors, [Formula: see text], between water and various materials were also derived using the Monte Carlo method. For the 60 MeV proton beam, [Formula: see text] and [Formula: see text] factors were within 1% from unity for all trial plastics. For the 226 MeV proton beam, experimental [Formula: see text] values deviated from unity by a maximum of about 1% for the three trial plastics and experimental results showed no advantage regarding which of the plastics was the most equivalent to water. Different magnitudes of corrections were found between Geant4 and FLUKA for the various materials due mainly to the use of different nonelastic nuclear data. Nevertheless, for the 226 MeV proton beam, [Formula: see text] correction factors were within 2% from unity for all the materials. Considering the results from the two Monte Carlo codes, PMMA and trial plastic #3 had the smallest [Formula: see text] values, where maximum deviations from unity were 1%, however, PMMA range differed by 16% from that of water. Overall, [Formula: see text] factors were deviating more from unity than [Formula: see text] factors and could amount to a few percent for some materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".