Low Doses of Radiation are Protective <i>in vitro</i> and <i>in vivo</i> : Evolutionary Origins
Bibliographic record
Abstract
Research reports using cells from bacteria, yeast, alga, nematodes, fish, plants, insects, amphibians, birds and mammals, including wild deer, rodents or humans show non-linear radio-adaptive processes in response to low doses of low LET radiation. Low doses increased cellular DNA double-strand break repair capacity, reduced the risk of cell death, reduced radiation or chemically-induced chromosomal aberrations and mutations, and reduced spontaneous or radiation-induced malignant transformation in vitro. In animals, a single low, whole body dose of low LET radiation, increased cancer latency and restored a portion of the life that would have been lost due to either spontaneous or radiation-induced cancer in the absence of the low dose. In genetically normal fetal mice, a prior low dose protected against radiation-induced birth defects. In genetically normal adult-male mice, a low dose prior to a high dose protected the offspring of the mice from heritable mutations produced by the large dose. The results show that low doses of low-LET radiation induce protective effects and that these induced responses have been tightly conserved throughout evolution, suggesting that they are basic responses critical to life. The results also argue strongly that the assumption of a linear increase in risk with increasing dose in humans is unlikely to be correct, and that low doses actually reduce risk.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".