Recoil proton, alpha particle, and heavy ion impacts on microdosimetry and RBE of fast neutrons: analysis of kerma spectra calculated by Monte Carlo simulation
Bibliographic record
Abstract
Fast neutrons (FN) have a higher radio-biological effectiveness (RBE) compared with photons, however the mechanism of this increase remains a controversial issue. RBE variations are seen among various FN facilities and at the same facility when different tissue depths or thicknesses of hardening filters are used. These variations lead to uncertainties in dose reporting as well as in the comparisons of clinical results. Besides radiobiology and microdosimetry, another powerful method for the characterization of FN beams is the calculation of total proton and heavy ion kerma spectra. FLUKA and MCNP Monte Carlo code were used to simulate these kerma spectra following a set of microdosimetry measurements performed at the National Accelerator Centre. The calculated spectra confirmed major classical statements: RBE increase is linked to both slow energy protons and alpha particles yielded by (n,α) reactions on carbon and oxygen nuclei. The slow energy protons are produced by neutrons having an energy between 10 keV and 10 MeV, while the alpha particles are produced by neutrons having an energy between 10 keV and 15 MeV. Looking at the heavy ion kerma from <15 MeV and the proton kerma from neutrons <10 MeV, it is possible to anticipate y* and RBE trends.Key words: fast neutron, kerma, microdosimetry, RBE, Monte Carlo.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".