Bibliographic record
Abstract
The critical infrastructure protection (CIP) set of standards is developed by the North American Electric Reliability Corporation (NERC) to ensure the protection of assets used to operate North America's bulk electric systems (BES). Any entity that owns or operates any type of BES in the United States and Canada must be compliant with the requirements of the NERC CIP standards. The purpose of this chapter is to provide an ample overview of the NERC CIP standards, to describe its relevance to the protection electric utility entities, to establish its harmonizing relation with the NIST cyber security framework, to provide a glimpse of its compliance requirements, and to investigate the gaps and prospects for workforce development training in this area of critical need. This chapter lays the foundation for opportunities in the design of automated NERC CIP standards compliance processes and toolkit, the feasibility study of adopting the CIP standards in other sectors, and the development of training materials in NERC CIP standards for workforce development.
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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 | 0.007 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".