<i>United States v. Osage Wind, LLC</i>: Wind Energy Being Blown Away by New Rules?
Bibliographic record
Abstract
When you think of Oklahoma energy, oil and gas comes to mind first.After all, oil and gas has been one of the most important contributing factors to economic growth in Oklahoma throughout the years. 1 Many have become accustomed to the view of a pump jack, or an oil and gas rig when they look out across the prairie.However, over the last few years, wind turbines have been added to that view, which should be no surprise since Oklahoma is "where the wind comes sweeping down the plains."Oklahoma ranks second in the nation for installed wind capacity, manufacturers turbines for use in and out of state at any of the seven manufacturing plants in the state, and surface lessors collectively receive $15-20 million annually in lease payments. 2Furthermore, the wind industry employs roughly 9,000 Oklahomans and close to a quarter of the state is powered by wind. 3 As the Morgen Potts is a recent graduate, and now owner of The Potts Law Firm in Norman, Oklahoma.It was an honor being picked to discuss this topic, and I thank Professor Tytanic and the ONE J staff for all their guidance during the writing process.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".