Reference radiation selection is confirmed as a significant source of relative biological effectiveness variation for neutrons
Bibliographic record
Abstract
Purpose To confirm that the selection of a reference radiation affects the magnitude and range of the relative biological effectiveness (RBE) evaluations of neutron test radiation. Particular attention was paid to the published thermal neutron RBE dataset that is highly variable, with values ranging from 5.4–51.1.Materials and methods This involved a dual approach of 1) reaffirming dicentric chromosome assay (DCA) dose-response curve differences for 60Co, 137Cs, and 250 kVp X-rays, and 2) recalculating maximum RBE at minimal doses (RBEM) for our previously reported neutron data, accompanied by an evaluation of reported studies that utilized two or more reference radiations.Results and conclusions The linear slope coefficient of the linear-quadratic dose-response curve, used to evaluate RBEM, was found to be significantly different for 60Co (0.0268 ± 0.0075 Gy−1) compared to 137Cs (0.0730 ± 0.0135, P < 0.01) and 250 kVp X-ray (0.1063 ± 0.0248, P < 0.01). Applying this finding to our previous thermal and fast neutron DCA evaluations, the RBEM varied by a factor of 2.7 for 60Co versus 137Cs, and by a factor of four for 60Co versus 250 kVp X-ray. A review of prior reported neutron RBEM literature affirmed the finding that reference radiation selection can influence RBEM magnitude. The selection of the reference radiation has implications for RBE evaluations of neutrons and other radiation qualities, as these RBE values underpin the radiation weighting factor, wR, which informs radiation protection measures both terrestrially and in space. These experiments and reanalysis reconfirm and strongly demonstrate that reference radiation selection is a significant determinant of RBE variability, especially as applied to neutrons.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".