Secure trilateral access control for network-integrated nuclear operation and condition-based nuclear maintenance
Bibliographic record
Abstract
The intent of this thesis research is to develop a new methodology to improve existing nuclear process in an efficient, precise, and cost-effective way. This thesis presents three new designs: Secure Trilateral Access Control (STAC), Network-Integrated Nuclear Operation (NINO), and network-driven Condition-Based Nuclear Maintenance (CBNM). STAC design has three tiers: Tier-1 ensures security controls of external accesses to the new nuclear network. Tier-2 ensures qualification controls for carrying nuclear operations. Tier-3 ensures qualification controls for nuclear maintenances. NINO design is to increase efficiency of conducting nuclear operations and ensure correctness of executing targeted operations. CBNM design is to increase efficiency and cost savings for conducting nuclear maintenance and schedule maintenance based on equipment conditions to avoid extremely expensive forced outages. Feasibility and practicality of these new designs are illustrated analytically and numerically in the thesis. The significance of these designs is tremendous, resulting in huge nuclear operation cost savings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".