The development of fetal dosimetry and its application to A-bomb survivors exposed in utero
Bibliographic record
Abstract
The cohort of the atomic bomb survivors of Hiroshima and Nagasaki comprises the major basis for investigations of health effects induced by ionising radiation in humans. To study the health effects associated with radiation exposure before birth, fetal dosimetry is needed if significant differences exist between the fetal absorbed dose and the mother's uterine dose. Combining total neutron and gamma ray free-in-air fluences at 1 m above ground with fluence-to-absorbed dose conversion coefficients, fetal doses were calculated for various exposure orientations at the ground distance of 1500 m from the hypocentres in Hiroshima and Nagasaki. The results showed that the mother's uterine dose can serve as a good surrogate for the dose of the embryo and fetus in the first trimester. However, significant differences exist between doses of the fetus of different ages. If the mother's uterine dose were used as a surrogate, doses to the fetus in the last two trimesters could be overestimated by more than 20 % for exposure orientations facing towards and away from the hypocentre while significantly underestimated for lateral positions relative to the hypocentre. In newer fetal models, the brain is modelled for all fetal ages. Brain doses to the 3-month fetus are generally higher than those to an embryo and fetus of other ages. In most cases, brain absorbed doses differ significantly from the doses to the entire fetal body. In order to accurately assess radiation effects to the fetal brain, it is necessary to determine brain doses separately.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".