EXTENDED CONVERSION COEFFICIENTS FOR USE IN RADIATION PROTECTION OF THE EMBRYO AND FETUS AGAINST EXTERNAL NEUTRONS FROM 10 MeV TO 100 GeV
Bibliographic record
Abstract
External neutron exposure is of concern in the environment and in some workplaces. Dose assessments for neutrons frequently rely on fluence-to-absorbed dose conversion coefficients. A problem of concern in radiation protection is exposure of pregnant women to ionizing radiation because of the high radiosensitivity of the embryo and fetus. While neutron fluence-to-dose conversion coefficients for adults are recommended in ICRP publications and ICRU reports, conversion coefficients for embryos and fetuses are not given in the publications. This study uses the Monte Carlo code MCNPX to determine mean absorbed doses to the embryo and fetus when the mother is exposed to neutron fields. A previous study has dealt with neutrons from 1 eV to 10 MeV. In this study, monoenergetic neutrons ranging from 10 MeV to 100 GeV are considered. The irradiation geometries include antero-posterior, postero-anterior, lateral, rotational, and isotropic. At each of these standard irradiation geometries, absorbed doses to the fetal brain and body are calculated for the embryo of 8 wk and the fetus of 3, 6, or 9 mo. Neutron fluence-to-absorbed dose conversion coefficients are derived for the four prenatal ages. The results showed that the fetus at about 3 mo of prenatal age should receive more radiation protection to prevent long-term brain damage. During prenatal life, the fetus generally receives the highest absorbed dose per unit neutron fluence for antero-posterior irradiation. In cases where the irradiation geometry is not specified or not adequately known, conversion coefficients of AP-irradiation can therefore be used in a conservative dose assessment of fetus exposure to external 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".