Bibliographic record
Abstract
Introduction Availability of reliable and real-time information is essential for the integration of intermittent renewable energy resources and improving the efficiency and performance of the aging electrical power grid. Hence, an integrated high-performance, pervasive, and secure communications infrastructure is one of the key foundations of smart grid evolution. Much of the recent standardization efforts, such as that led by the US National Institute of Standards and Technology (NIST) [1], and the IEEE P2030 [2], has focused on defining high-level, technology-neutral architecture and reference models for smart grid communications networks. An abstract architecture offers a framework of logical connections between different system domains and high-level requirements to be followed by specific solutions. While such a conceptual architectural model is imperative for ensuring interoperability, it is not mapped directly to specific solutions, nor does it address detailed implementation issues. This chapter is focused on physical communications and access techniques that support current and upcoming smart grid applications. We discuss in detail a variety of communications media and technologies and how they can be applied in smart grid communications networks. The rest of this section provides some background information on our discussion. Section 5.1.1 begins with a look at the history of utility communications networks. Such knowledge is important in understanding the existing utility communications infrastructure and necessary improvements needed en route to smart grid. The key objectives in establishing smart grid communications networks are discussed in Section 5.1.2, followed by data classification and requirements in smart grid in Section 5.1.3.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.015 |
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".