Fluence correction factor for graphite calorimetry in a clinical high-energy carbon-ion beam
Bibliographic record
Abstract
The aim of this work is to develop and adapt a formalism to determine absorbed dose to water from graphite calorimetry measurements in carbon-ion beams. Fluence correction factors, , needed when using a graphite calorimeter to derive dose to water, were determined in a clinical high-energy carbon-ion beam. Measurements were performed in a 290 MeV/n carbon-ion beam with a field size of 11 × 11 cm 2 , without modulation. In order to sample the beam, a plane-parallel Roos ionization chamber was chosen for its small collecting volume in comparison with the field size. Experimental information on fluence corrections was obtained from depth-dose measurements in water. This procedure was repeated with graphite plates in front of the water phantom. Fluence corrections were also obtained with Monte Carlo simulations through the implementation of three methods based on (i) the fluence distributions differential in energy, (ii) a ratio of calculated doses in water and graphite at equivalent depths and (iii) simulations of the experimental setup. The term increased in depth from 1.00 at the entrance toward 1.02 at a depth near the Bragg peak, and the average difference between experimental and numerical simulations was about 0.13%. Compared to proton beams, there was no reduction of the due to alpha particles because the secondary particle spectrum is dominated by projectile fragmentation. By developing a practical dose conversion technique, this work contributes to improving the determination of absolute dose to water from graphite calorimetry in carbon-ion beams.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".