A survey on artificial intelligence in nuclear emergency preparedness and response
Bibliographic record
Abstract
Nuclear energy is considered one of the safest sources of energy in the world, however there is a low probability of occurrence of a nuclearaccident that might trigger a nuclear emergency. As of December 2023, there are 413 operating nuclear power plants in 31 different countries,and although the design of these nuclear power plants is based upon the concepts of Defence in Depth with very conservative assumptions,the hazard from natural disaster, human error and non-vigilant actions might results in nuclear emergency. Since the last majornuclearaccident Fukushima Daichi in 2011, many researchers have highlighted the need for more advanced and automated system tosupport the emergency preparedness and response in optimizing the protective action strategies. In this study we introduce the concept ofapplying artificial intelligence to enhance the readiness and the response capability during nuclear emergency. Through the predictability and computational features of AI models and machine learning techniques, the EPR systems can be enhanced by improving the hazardassessment, optimizing the dose projections models, enhancing the protective actions strategies and improving the decision-making process. However, this application also presents challenges such as data reliability, cybersecurity and regulatory compliance. The results of this studyhighlight the significance of applying AI in EPR and the need for further research on this application with a particular focus on addressingthese challenges to ensure safe implementation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".