Implementation of the integrated approach in different types of exposure scenarios
Bibliographic record
Abstract
The International Commission on Radiological Protection (ICRP) recognises three types of exposure situations: planned, existing, and emergency. In all three situations, the release of radionuclides into the natural environment leads to exposures of non-human biota, as well as the potential for exposures of the public. This paper describes how the key principles of the ICRP system of radiological protection apply to non-human biota and members of the public in each of these exposure situations. Current work in this area within ICRP Task Group 105 is highlighted. For example, how simplified numeric criteria may be used in planned exposure situations that are protective of both the public and non-human biota. In emergency exposure situations, the initial response will always be focused on human protection; however, understanding the potential impacts of radionuclide releases on non-human biota will likely become important in terms of communication as governments and the public seek to understand the exposures that are occurring. For existing exposure situations, there is a need to better understand the potential impacts of radionuclides on animals and plants, especially when deciding on protective actions. Understanding the comparative impacts from radiological, non-radiological, and physical aspects is often important in managing the remediation of legacy sites. Task Group 105 is making use of case studies of how exposure situations have been managed in the past to provide additional guidance and advice for the protection of non-human biota.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".