Old Data—New Concepts: Integrating “Indirect Effects” Into Radiation Protection
Bibliographic record
Abstract
PURPOSE: To address the following key question, what are the consequences of nontargeted and delayed effects for linear nonthreshold models of radiation risk? This paper considers low-dose "indirect" or nontargeted effects and how they might impact radiation protection, particularly at the level of the environment. Nontargeted effects refer to effects in cells, tissues, or organisms that were not targeted by irradiation and that did not receive direct energy deposition. They include genomic instability and lethal mutations in progeny of irradiated cells and bystander effects in neighboring cells, tissues, or organisms. Low-dose hypersensitivity and adaptive responses are sometimes included under the nontargeted effects umbrella, but these are not considered in this paper. Some concepts emerging in the nontargeted effects field that could be important include historic dose. This suggests that the initial exposure to radiation initiates the instability phenotype which is passed to progeny leading to a transgenerational radiation-response phenotype, which suggests that the system response rather than the individual response is critical in determining outcome. CONCLUSION: Nontargeted effects need to be considered, and modeling, experimental, and epidemiological approaches could all be used to determine the impact of nontargeted effects on the currently used linear nonthreshold model in radiation protection.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.014 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".