A Model for the Induction of DNA Damages by Fast Neutrons and their Evolution into Cell Clonogenic Inactivation
Bibliographic record
Abstract
It has been long stated that cellular inactivation through neutron irradiation is mainly caused by energy deposition in DNA molecules from recoiled secondary charged particles. Complexities associated with neutrons, such as the generally broad energy spectrum and the inherently wide energy spectrum of the induced charged particles, not to mention that the dependence of cellular inactivation by charged particles on radiation quality is yet to be fully understood, make it difficult to check this statement. Recently a molecular model has been proposed that improves the quantitative explanation of the dependence of cellular inactivation by charged particles on radiation quality. An attempt was made to apply this model for analysis of neutron cellular inactivation. As a preliminary result it is suggested that neutron cellular inactivation is caused not only by secondary charged particles but also by an "atomic deletion" effect, generated by a stripped atom recoiling from a DNA molecule. This effect seems to be of significant importance, the inactivation cross section of this effect for fission neutrons is as much as 15% (aerobic conditions) or 55% (hypoxic) of the total, and the severity of one occurrence of atomic deletion by a single neutron is estimated as much as 3.1 +/- 1.1 times (aerobic) or 6.8 +/- 1.2 times (hypoxic) higher than the severity of one event by a single track of a charged particle interacting with DNA.
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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 | 0.000 | 0.001 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".