Bibliographic record
Abstract
When people discuss the risks associated with low doses of ionizing radiation, central to the discussion is the definition of a low dose and the nature of harm. Standard answers such as "doses below 0.1 Gy are low" or "cancer is the most sensitive measure of harm" obscure the complexity within these seemingly simple questions. This paper will discuss some of the complex issues involved in determining risks to human and nonhuman species from low-dose exposures. Central to this discussion will be the role of communicable responses to all stressors (often referred to as bystander responses), which include recently discovered epigenetic and nontargeted mechanisms. There is a growing consensus that low-dose exposure to radiation is but one of many stressors to impact populations. Many of these stressors trigger responses that are generic and not unique to radiation. The lack of a unique radiation signature makes absolute definition of radiation risk difficult. This paper examines a possible new way of defining low dose based on the systemic response to the radiation. Many factors will influence this systemic response and, because it is inherently variable, it is difficult to predict and so makes low-dose responses very uncertain. Rather than seeking to reduce uncertainty, it might be valuable to accept the variability in outcomes, which arise from the complexity and multifactorial nature of responses to stressors.
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 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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.004 | 0.014 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".