The Method of Cyber Awareness Analysis of an Energy Facility
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
The article proposes to analyze cyber-situational awareness of an energy facility in three stages. There are i) analysis of cyber threats to the energy infrastructure; ii) modeling of extreme situations scenarios in the energy sector caused by the implementation of the cyber threats; iii) risk assessment of the cybersecurity disruption to energy infrastructure. Three methods are presented, corresponding to each stage. The authors propose to apply semantic modeling methods to analyze the impact of cyber threats to energy facilities, taking into account energy security within the presented approach. Such methods show their effectiveness in the absence or incompleteness of data for modeling the behavior of systems, which defies formal description or accurate forecasting. The presented approach to the cyber situational awareness analysis of energy facilities considered as a synthesis of cybersecurity and situational awareness studies, characterized by the use of semantic modeling methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it