Approaches to the definition of potentially exposed groups and potentially exposed populations of biota in the context of solid radioactive waste
Bibliographic record
Abstract
A methodology for addressing the biosphere in safety assessments for solid radioactive waste disposal was developed through theme 1 of the IAEA coordinated research project on BIOsphere Modelling and ASSessment (BIOMASS) that ran from 1996 to 2001. This methodology provided guidance on how the biosphere can be addressed in safety assessments for disposal of solid radioactive waste. Since the methodology was developed, it has proven useful and has been widely referenced in assessments in a diversity of contexts encompassing both near-surface and deep geological disposal of solid radioactive waste. The principles that could be adopted for defining potentially exposed groups (PEGs) were an important aspect in the original BIOMASS methodology as the endpoint of an assessment usually includes the evaluation of individual dose or risk to human health. Identification of PEGs and definition of their characteristics are usually made to be consistent with the biosphere system description being developed, acknowledging that due to inherent uncertainties in projecting future human behaviour, the biosphere models adopted for assessing safety of a disposal system can only be illustrative. Since the publication of the original BIOMASS methodology, consideration has been extended to include potentially exposed populations of biota (PEPs), in the context of dose assessment and protection of the environment. Considering the need for the development of transfer pathways from a source term to an end point (for either PEGs or PEPs), the exposure modes that may occur and those to be assessed quantitatively should be identified. Within an expert working group (WG6) of the second phase of the IAEA coordinated project Modelling and Data for Radiological Impact Assessments (MODARIA II), the experience of participating organisations has been collected on topics associated with the definition of PEGs and PEPs using a questionnaire. The objective of the questionnaire was to review the current status and on-going discussions on the handling of issues related to definitions of PEGs and PEPs as an input to the development of biosphere models for assessing radiological impacts on human health and the environment. The answers received to the questionnaire provided a clear overview of the progress that has been made since the original BIOMASS methodology was published, together with the lessons learned from the application of that methodology in the development of safety cases. This paper summarises the questionnaire responses in five subject areas: (1) environment of the PEGs and its evolution; (2) linking the choice of PEGs to these environments; (3) food habits and consumption rates; (4) populations of non-human biota (PEPs) and (5) national and international regulations and guidance. We illustrate how the results of the questionnaire have been used to enhance the original BIOMASS methodology (IAEA Enhanced BIOMASS Methodology Report in press).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".