Dosimetric and microdosimetric characterization of blood samples exposed to primarily fast-neutron fields within the ZED-2 reactor by Monte Carlo simulations
Bibliographic record
Abstract
Neutrons are found in a variety of settings, namely reactor workplaces, radiotherapy facilities, accelerator facilities, and spacecraft, among others. This study, carried out in support of the neutron radiobiology research program at Canadian Nuclear Laboratories, characterized the mixed gamma ray and fast-neutron field that traversed the human blood samples exposed within the Zero Energy Deuterium (ZED-2) reactor. This characterization quantified the fluence and energy distribution of neutrons and gamma rays that traversed each blood sample, as well as the absorbed dose delivered. The development of a reactor physics model of ZED-2 employed the Monte Carlo N-Particle 6 (MCNP6) version 2.0 radiation transport code. Using these distributions, further simulations were carried out to determine the quality factor and other microdosimetric quantities of dose delivery to a blood sample. A quality factor of about 15 was evaluated for exposures of blood sample to the mixed neutron and gamma ray field, providing about 90% and 10% of the absorbed dose, respectively. The absorbed dose delivered by the mixed field varied between 33 and 915 mGy. The quality factor was about 16 for the neutron field alone. The irradiating neutron field had a fluence-weighted mean energy of 0.63 MeV, a negligible thermal component, and a continuum through the epithermal and fast-neutron energy ranges. The irradiating gamma ray field had a fluence-weighted mean energy of 1.45 MeV. These characterizations will allow accurate dose response assessments and comparisons with subsequent blood cell biomarker assessments of relative biological effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".