Herding Cats: What to Do When States Get in the Way of National Energy Policy
Bibliographic record
Abstract
Shifting the United States' primary source of electricity from non-renewable energy to renewable energy requires expanded capacity to facilitate long-range transmission from regions where it can be efficiently produced to large population centers where it will be used In Piedmont Environmental Council v. FERC the Fourth Circuit Court of Appeals recently held that the Federal Energy Regulatory Commission could not site new interstate transmission lines if a state had already denied approval. Denying FERC's authority to site new transmission lines within NationalCorridors will significantly influence the growth of renewable energy.This Recent Development explores the impact of the court's ruling on the spread of renewable energy and offers a legislative solution to potential problems.' 0 See BLACKS LAW DICTIONARY 1513 (9th ed.2009) (defining "site" as "a piece of property set aside for a specific use.")."16 U.S.C. § 824p(b).12 Id.§ 824p(b)(C)(i).There are a number of other circumstances that will trigger FERC's authority to site interstate transmission lines.Those circumstances are listed at 16 U.S.C. § 824p(b).National interest electricity transmission corridors are "any geographic area experiencing electric energy transmission capacity constraints or congestion that adversely affects consumers."16 U.S.C. § 824p(a)(2).Currently, national interest electricity transmission corridors are comprised of parts of: Delaware, Ohio, Maryland, 146 [VOL.11: 145
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".