Effects of weather on avian migration at proposed ridgeline wind energy sites
Bibliographic record
Abstract
Abstract With the popularity of wind energy increasing globally, concerns surfaced in the 1980s as to the potential adverse effects of wind turbines on migrating birds. Understanding how weather conditions influence passage rates can help determine the potential for increased avian–turbine collisions. Using vertical and horizontal mounted marine radars, raptor stand watch observations, and portable handheld weather stations, we studied how temperature, cloud cover, barometric pressure, wind direction, and wind speed affected avian passage rates and height of migrants over 3 ridges (Wartenbe, North Dokie, and South Dokie) being developed for wind energy in northern British Columbia. Using an Akaike's Information Criterion (AIC), we determined that a reduced model combining wind speed, barometric pressure, and cloud cover was best at explaining and predicting higher passage rates (expressed as no. birds/hr) in the fall migration for both diurnal and nocturnal migrants. Wind speed proved the most important predictor of passage rates for spring nocturnal migrants and a combination of cloud cover, temperature, and wind direction for diurnal spring migrants. Wind speed also predicted decreases in flight altitude among nocturnal migrants but increased altitude in diurnal migrants. This information coupled with migration timing and topographical areas of higher migrant activity can be useful to wind energy proponents who wish to mitigate collision risk with migrating birds. © 2011 The Wildlife Society.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".