Ionizing radiation exposure effects across multiple generations: evidence and lessons from non-human biota
Bibliographic record
Abstract
A Task Group (TG121) of the International Commission on Radiological Protection (ICRP) Committee 1 was launched in 2021 to study the effects of ionizing radiation in offspring and next generations. In this report, we summarize the evidence of multi- and trans-generational effects in non-human biota species that was discussed at the ICRP workshop entitled "Effects of Ionizing Radiation Exposure in Offspring and Next Generations" in June 2022. Epigenetic changes, including changes in DNA methylation, have been observed in trans- and multi-generational irradiation studies in both plants and animals. There were also reports of changes in offspring survival and reproduction. The reported evidence for altered reproduction is an area of potential concern, due to possible effects at the population or ecosystem level. Different considerations are also discussed regarding non-human biota data, such as transferability of data between different species or extending knowledge to humans, differences in species radiosensitivity, the presence of adaptive responses, and dose reconstruction for exposures that occur across multiple generations. Overall, there is a diverse range of available data of the effects in non-human biota, and it will require careful consideration when incorporating this evidence into the system of radiological protection of humans and of the environment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".