QNP_SHELL: A computerized tool for improving decision-making skills for nuclear power plant operators
Bibliographic record
Abstract
Decision-making in complex systems such as nuclear power plants (NPPs) is a difficult task at best. The safety and integrity of many such high-capital cost-intensive installations depend on the operator’s capability to correctly diagnose and take appropriate measures to avoid any abnormal operations of an NPP. Therefore, the role of the expert systems in the offline training programs for the operators is ever increasing. In this paper, we describe the development of an expert system, “QNP_SHELL,” to assist, offline QNPP operators and plant personnel in a better familiarization to infer the anticipated and foreseen malfunctions from the observed symptoms. QNP_SHELL’s inferencing mechanism is of the “Rule-based” type and to search the knowledge base it adopts the “Depth First” technique. The diagnostic performance of the trainee operators using QNP_SHELL on various accidents at QNPP has been found, through both the qualitative and quantitative evaluations, satisfactory.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".