Adaption by Low Dose Radiation Exposure: A Look at Scope and Limitations for Radioprotection
Bibliographic record
Abstract
The procedures and dose limitations used for radiation protection in the nuclear industry are founded on the assumption that risk is directly proportional to dose, without a threshold. Based on this idea that any dose, no matter how small, will increase risk, radiation protection regulations generally attempt to reduce any exposure to "as low as reasonably achievable" (ALARA). We know however, that these regulatory assumptions are inconsistent with the known biological effects of low doses. Low doses induce protective effects, and these adaptive responses are part of a general response to low stress. Adaptive responses have been tightly conserved during evolution, from single celled organisms up to humans, indicating their importance. Here we examine cellular and animal studies that show the influence of radiation induced protective effects on diverse diseases, and examine the radiation dose range that is effective for different tissues in the same animal. The concept of a dose window, with upper and lower effective doses, as well as the effect of multiple stressors and the influence of genetics will also be examined. The effect of the biological variables on low dose responses will be considered from the point of view of the limitations they may impose on any revised radiation protection regulations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".