Climate Teleconnection Influences on Low-Level Wind Speeds over the Great Plains
Bibliographic record
Abstract
abstract: The Great Plains region of the central United States and southern Canada is a promising location for wind energy resource development. Wind energy site assessments and forecasts can benefit from better understanding the variability that may result from several teleconnections affecting North America. This thesis investigates how the El Niño/Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Pacific/North American Pattern (PNA) impact mean monthly wind speeds at 850 hPa over the Great Plains. Using wind speeds from the NCAR/NCEP Reanalysis 1, correlations were computed between the mean monthly wind speeds and average monthly teleconnection index values. A difference of means test was used to compute the change in wind speeds between the positive and negative phases of each index. ENSO was not found to have a significant impact on wind speeds, while the NAO and PNA patterns weakly affected wind speeds. The NAO index was positively (negatively) correlated with wind speeds over the northern (southern) plains, while the PNA index was negatively correlated with wind speeds over most of the plains. Even a small change in wind speed can have a large effect on the potential power output, so the effects of these teleconnections should be considered in wind resource assessments and climatologies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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; 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".