Factors influencing avian movement patterns around proposed ridgeline wind farm sites in British Columbia, Canada.
Bibliographic record
Abstract
Due to the concerns over global warming, the demand for green renewable energy is escalating. Wind farms in Canada are being approved for construction at an exponential rate. Because large scale wind energy is relatively new technology, concerns have surfaced over the impact of wind turbines on aerial fauna, such as birds and bats. Proper monitoring protocols designed to understand the animals' behaviour around these structures are thus vital to identify potential risks. I monitored bird migration at three mountain ridges near Chetwynd, British Columbia. By using a combination of radar to track nocturnal migrants and stand watches to track diurnal migrants, I investigated how birds use landscape features during migration and how these movement patterns are influenced by regional weather systems. I found evidence that raptor movement patterns were influenced by topography, as diurnally-migrating raptors tend to move in concentration parallel to the windward edge of the ridgelines. Conversely, nocturnal movement patterns appeared less influenced by local topography than diurnal migrants. After collecting weather data and examining their effect on avian passage rates, I found that a combination of barometric pressure, cloud cover and wind speed was generally best able to explain and predict passage rates (wind speed being a strong variable). By understanding the spatial and temporal patterns of migration, wind farm proponents can develop mitigation strategies to minimize collision risk.
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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 | 0.000 | 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 | 0.002 | 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".