Effect of the Surveillance Test Frequency of SDS1 on the Core Damage Probability
Bibliographic record
Abstract
A new technique for analyzing the effect of testing on shutdown system (SDS) number 1 (SDS1) in Canadian deuterium uranium (CANDU) nuclear power plants is proposed. The effect of the test on the core damage probability is quantified using a Markov process model. The model is used to derive the effect of the test frequency on the unavailability and the spurious reactor trip probability. Two core damage scenarios are considered: one from a process failure with the unavailable SDS and the other from a spurious reactor trip. The Markov process model is then used with the core damage scenarios to analyze the effect of the test frequency on the core damage probability. The quantified core damage probabilities indicate that performing more frequent surveillance tests does not necessarily decrease the risk. In fact, there exists an optimal test frequency beyond which the probability of core damage starts to increase. This optimal test frequency is of significance in practice.
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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 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 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".