Safety Argumentation for a Nuclear Reactor Protection System -- an Assessor's View
Bibliographic record
Abstract
Structured safety argumentation has several advantages over safety demonstrations provided through a free text form.However, there are few publicly available examples of broadly accepted safety assurance cases with sufficient detail to demonstrate best practice.Furthermore, they usually reflect the system developers' viewpoint.This paper presents simplified extracts of a safety assurance case from a case study that uses an assessor's viewpoint to structure the argument.The case study is based on relevant sections of US Nuclear Regulatory Commission regulation.The argument is partial and focuses on the conceptual design level of the "trip" safety function allocated to the Reactor Protection System of a nuclear power plant.Reflections and general observations from the discussion with an expert assessor aim to support readers with practical considerations for similar safety assurance cases.
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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 | 0.033 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".