FERC regulation—electricity: FERC and NERC speed processing of electric reliability violations
Bibliographic record
Abstract
Abstract The North American Electric Reliability Corporation (NERC) is the organization responsible for ensuring the reliability of the bulk‐power system (BPS) in the United States, Mexico, and Canada. NERC recently received approval from the Federal Energy Regulatory Commission (FERC) to streamline the process for tracking and reporting violations of electric reliability standards. On March 15, FERC issued an order conditionally approving the proposed “Find, Fix, Track, and Report” (FFTR) process to expedite the enforcement process for violations of NERC Reliability Standards that pose lesser risk to the BPS. 1 In its approval of NERC's new process, however, FERC included several conditions that narrow the range of potential violations eligible for the expedited process. FERC Chairman Jon Wellinghoff hailed the new process as “a major change in how [FERC] will enforce compliance with the Reliability Standards going forward.” 2
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.
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 | 0.035 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.025 | 0.010 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".