Dosimetric and microdosimetric analyses for blood exposed to reactor-derived thermal neutrons
Bibliographic record
Abstract
Thermal neutrons are found in reactor, radiotherapy, aircraft, and space environments. The purpose of this study was to characterise the dosimetry and microdosimetry of thermal neutron exposures, using three simulation codes, as a precursor to quantitative radiobiological studies using blood samples. An irradiation line was designed employing a pyrolytic graphite crystal or—alternatively—a super mirror to expose blood samples to thermal neutrons from the National Research Universal reactor to determine radiobiological parameters. The crystal was used when assessing the relative biological effectiveness for dicentric chromosome aberrations, and other biomarkers, in lymphocytes over a low absorbed dose range of 1.2–14 mGy. Higher exposures using a super mirror will allow the additional quantification of mitochondrial responses. The physical size of the thermal neutron fields and their respective wavelength distribution was determined using the McStas Monte Carlo code. Spinning the blood samples produced a spatially uniform absorbed dose as determined from Monte Carlo N-Particle version 6 simulations. The major part (71%) of the total absorbed dose to blood was determined to be from the 14 N(n,p) 14 C reaction and the remainder from the 1 H(n, γ ) 2 H reaction. Previous radiobiological experiments at Canadian Nuclear Laboratories involving thermal neutron irradiation of blood yielded a relative biological effectiveness of 26 ± 7. Using the Particle and Heavy Ion Transport Code System, a similar value of ∼19 for the quality factor of thermal neutrons initiating the 14 N(n,p) 14 C reaction in soft tissue was determined by microdosimetric simulations. This calculated quality factor is of similar high value to the experimentally-derived relative biological effectiveness, and indicates the potential of thermal neutrons to induce deleterious health effects in superficial organs such as cataracts of the eye lens.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".