Bibliographic record
Abstract
ABSTRACT Wind turbines have been hypothesized to affect bat populations; however, no comprehensive analysis of bat mortality from the operation of wind turbines in Canada has been conducted. We used data from carcass searches for 64 wind farms, incorporating correction factors for scavenger removal, searcher efficiency, and carcasses that fell beyond the area searched to estimate bat collision mortality associated with wind turbines in Canada. On average, 15.5 ± 3.8 (95% CI) bats were killed per turbine per year at these sites (range = 0−103 bats/turbine/yr at individual wind farms). Based on 4,019 installed turbines (the no. installed in Canada by Dec 2013), an estimated 47,400 bats (95% CI = 32,100−62,700) are killed by wind turbines each year in Canada. Installed wind capacity is growing rapidly in Canada, and is predicted to increase approximately 3.5‐fold over the next 15 years, which could lead to direct mortality of approximately 166,000 bats/year. Long‐distance migratory bat species (e.g., hoary bat [ Lasiurus cinereus ], silver‐haired bat [ Lasionycteris noctivagans ], eastern red bat [ Lasiurus borealis ]) accounted for 73% of all mortalities. These species are subject to additional mortality risks when they migrate into the United States. The little brown myotis ( Myotis lucifugus ), which was listed as Endangered in 2014 under the Species At Risk Act (SARA), accounted for 13% of all mortalities from wind turbines, with most of the mortality (87%) occurring in Ontario. Population‐level impacts may become an issue for some bat species as numbers of turbines increase. © 2016 The Wildlife Society.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".