QNP_SHELL: A computerized tool for improving decision-making skills for nuclear power plant operators
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 | 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