Simulating the influences of bat curtailment on power production at wind energy facilities
Bibliographic record
Abstract
Abstract The development and expansion of wind energy is considered a key threat to bat populations in North America and globally. Several approaches to mitigating the impacts of wind energy development on bat populations have been developed, including curtailing wind turbine operation at night during lower wind speeds when bats are thought to be more active. Blanket curtailment approaches have shown substantial promise in reducing bat fatalities at wind energy facilities, but they also reduce the amount of energy extracted from the wind by turbines. A related approach, referred to as smart curtailment, uses bat activity and other variables to predict when bats will be at the greatest risk at a given wind facility. In some contexts, a smart curtailment approach might reduce bat fatalities while also reducing energy loss relative to blanket curtailment. However, it has not been clear how to compare blanket curtailment and smart curtailment approaches in terms of annual energy production at wind facilities. Here, we describe a new approach to simulating the influence of blanket and smart curtailment approaches on energy production at wind energy facilities, and demonstrate the approach using 6 wind energy development areas in the Canadian province of Alberta. We show how stakeholders involved can explore the potential influences of various kinds of bat activity on energy production. We present the results of our Alberta analysis and conclude with some caveats and recommendations for future work on simulating the influences of bat curtailment on energy production at wind energy facilities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".