Utilizing spatial analysis to predict optimal locations for wind energy production in Washington state and British Columbia
Bibliographic record
Abstract
Wind turbines can harness a free, readily-available natural resource to produce energy that feeds the ever-growing energy demands of the United States and Canada. Wind energy is a renewable resource that could be used to reduce reliance on fossil fuels and limit the production of greenhouse gas emissions that contribute to climate change. In the Pacific Northwest, a region dominated by the production of hydroelectricity, energy companies are beginning to invest and develop wind energy production. In Washington state, wind energy production accounts for 7.2% of the state’s total power production, while British Columbia produces 1.6% of its energy using wind power. In an effort to expand renewable energy production, this study utilized a multi-criteria evaluation to predict optimal future geographic locations for wind energy facilities in Washington and British Columbia. Using a geographic information system, variables on the region’s climate, topography, and anthropogenic developments were weighted and analyzed to identify possible locations for future wind development. Optimal areas had high aboveground wind speeds, non-forested land cover, low slope angles, and were in close proximity to existing power transmission lines. Additionally, priority was given to sites that were located away from urban centers in an effort to reduce noise and visual pollution. Early findings suggest that both Washington and British Columbia possess the ability to expand current wind energy production and supplement the use of hydroelectricity in this region.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".