A new assessment method for demonstrating the sufficiency of the safety assessment and the safety margins of the geological disposal system
Bibliographic record
Abstract
A new method for demonstrating the sufficiency of the safety assessment and safety margins of the geological disposal system has been developed. The method is based on an existing comprehensive sensitivity analysis method and can systematically identify the successful conditions, under which the dose rate does not exceed specified safety criteria, using analytical solutions for nuclide migration and the results of a statistical analysis. The successful conditions were identified using three major variables. Furthermore, the successful conditions at the level of factors or parameters were obtained using relational equations between the variables and the factors or parameters making up these variables. In this study, the method was applied to the safety assessment of the geological disposal of transuranic waste in Japan. Based on the system response characteristics obtained from analytical solutions and on the successful conditions, the classification of the analytical conditions, the sufficiency of the safety assessment and the safety margins of the disposal system were then demonstrated. A new assessment procedure incorporating this method into the existing safety assessment approach is proposed in this study. Using this procedure, it is possible to conduct a series of safety assessment activities in a logical manner.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".