Demonstrating the use of a framework for risk-informed decisions with stakeholder engagement through case studies for NORM and nuclear legacy sites
Bibliographic record
Abstract
The international community has come a long way in developing a consensus that the remediation and management of naturally occurring radioactive materials and nuclear legacy sites will benefit from the use of the framework for risk-informed decision-making. Such a framework should ideally integrate risk assessment and decision-making. The framework presented in this paper specifically addresses the needs and expectations in the wider socio-economic and environmental context, as well as a narrower human health context. The framework was demonstrated as part of the International Atomic Energy Agency's second Modelling and Data for Radiological Impact Assessments Programme. Three case studies, which have used or could use this integrative approach, are used for illustration. The first concerns remediation from uranium mining activities at Beaverlodge Lake in northern Saskatchewan, Canada, engaging stakeholders (also called 'interested parties') in the decision-making process on further options. The second case study suggests how decision analysis could support the selection of the best option for waste disposal for uranium ore processing at Žirovski vrh, Slovenia, taking into account a potential landslide and migration of waste throughout the adjacent valley in the event of flooding. The third case study presents the process and results of radiological safety assessment of the Kepkensberg sludge basin in Tessenderlo area, Belgium both before and after the disposal of material from remediation of the nearby Winterbeek River. It illustrates how such assessments could interface with decision analysis for the purpose of supporting the regulatory decisions related to future approval of a waste disposal option. Results show that formal stakeholder engagement in decision analysis provides a strong contribution to objective, robust, and transparent decision-making not only for radiation protection area but also in others where health and environmental impacts are of concern. A number of recommendations for future work have also been made.
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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 | 0.081 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".