P-2a-89 Effects of Low-Dose Ionising Radiation
Bibliographic record
Abstract
Recent years there have been a number of discussions about the magnitude of the health risks resulting from the exposure to low levels of ionising radiation mainly within the American Health Physics Society, the French Academy of Sciences and the Canadian Radiation Protection Association. Essentially, the debate has centred on the question of linearity, i.e., whether to assume that the probability of radiation-induced cancer at low doses is proportional to the radiation dose received, without any threshold below which there is no risk, or to assume that a threshold does exist. In view of the current status of knowledge and of the established ethical precautionary principle, the use of the LNT assumption and the current ”ICRP system of protection ” is justified according to the author’s view for radiation protection purposes. It has also a good acceptance among the health physicists all over the world. However, this approach to limiting the radiation risk should be used with great care. The collective dose should not be used to predict future detriment in the form of mortality numbers at very low doses say below a few millisieverts to large segments of populations. Recently, there have been a number of discussions about the magnitude of the health risks resulting from the exposure to low levels of ionizing radiation mainly within the American Health Physics Society, the French Academy of Sciences and the Canadian Radiation Protection Association. Essentially, the debate has centered on
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.008 |
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".