Investigation on the Impacts of Smart Curtailment for Bat Fatality Mitigation in Alberta
Bibliographic record
Abstract
Abstract. As wind energy continues to expand in Canada, it is increasingly important to balance power generation with wildlife conservation. For migratory bat species, the risk of interactions with wind turbines varies throughout the year. In response to environmental conditions, curtailing turbine operation during periods of higher risk has been shown to reduce bat fatalities. This study models seasonal turbine curtailment scenarios across wind farms in the Canadian province of Alberta to estimate the resulting energy and economic impacts. High-resolution weather data were used to reconstruct complete wind speed records and simulate turbine output. The modeled power output was closely aligned with real production data reported by the province's energy operator. Results indicate that curtailment outcomes vary significantly depending on wind speed thresholds, seasonal wind conditions, and curtailment duration. Across all scenarios, smart curtailment reduced energy and financial losses by 20–40 % compared to blanket curtailment, highlighting the benefits of using meteorological and behavioral triggers. These findings provide practical insights for minimizing energy loss while supporting conservation goals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".