Development of an integrated framework to implement the nuclear safety goals with various safety criteria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The question “How safe is safe enough?” has been raised since the start of nuclear power plant (NPP) operations in the 1950s, and to address this issue, the Nuclear Regulatory Commission (NRC) of the United States (U.S.) introduced the concept of safety goals in the mid-1980s. In the U.S., the safety goal is called the 0.1 % rule because it requires that the additional risk imposed on individuals and society by the operation of a new NPP should not exceed one-tenth of one percent (0.1 %) of the total risk resulting from other factors to which members of the U.S. population are generally exposed. Nordic countries Sweden and Finland have taken a different approach to setting safety goals for NPPs than the U.S. by introducing requirements for the amount and frequency of cesium-137 (Cs-137) releases. In the case of the Republic of Korea, the regulatory body introduced nuclear safety goals following amendments to the Nuclear Safety Act in 2016. Korea adopted the same safety goal of the 0.1 % rule of the U.S. and simultaneously introduced the Cs-137-related safety goal, which requires that the total frequency of NPP accidents in which the release of Cs-137 exceeds 100 TBq is less than or equal to 1.0E-6 per year. The latter was introduced as a safety goal to protect the environment. Furthermore, while Canada, which has also formally adopted the Cs-137-related safety goal, and Finland require this goal to be applied only to new NPPs, the Korean regulatory body requires the Cs-137-related safety goal to be applied to both operating and new NPPs. Thus, various safety criteria are used in the Korean nuclear safety goals. The use of various safety criteria as in the Korean nuclear safety goals might cause several issues in the practical application of those safety criteria. In this paper, I examine these issues from three main perspectives: (1) consistency, (2) assessment framework, and (3) practical application. I propose several methods to resolve these issues, and also recommend a new integrated framework to implement the safety goals with various safety criteria based on the proposed methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 it