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Record W4389548269 · doi:10.1080/09553002.2023.2281512

Ionizing radiation exposure effects across multiple generations: evidence and lessons from non-human biota

2023· review· en· W4389548269 on OpenAlexaff
Shayenthiran Sreetharan, Sandrine Frelon, Nele Horemans, Patrick Laloi, Sisko Salomaa, Christelle Adam‐Guillermin

Bibliographic record

VenueInternational Journal of Radiation Biology · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsMcMaster UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsOffspringBiologyIonizing radiationBiotaPopulationReproductionEcologyToxicologyEnvironmental healthGeneticsMedicinePregnancyIrradiation

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.067
GPT teacher head0.411
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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