Home-range habitat selection by Ferruginous Hawks in western Canada: implications for wind-energy conflicts
Bibliographic record
Abstract
Global wind-energy development has increased exponentially in recent decades and is expected to double in capacity in Canada by 2040. Wind-farm development has significant implications for wildlife, particularly for raptors, where injury or death from turbine strikes and other cumulative effects are well documented. Minimizing conflict is important for species at risk, such as the Ferruginous Hawk (Buteo regalis), because negative impacts from wind farms may hinder conservation and recovery actions. Understanding Ferruginous Hawk habitat selection is needed to assess the potential spatial overlap with wind-farm development and make spatially explicit predictions of conflict risk. Our objectives were (1) to develop a predictive map of habitat selection by Ferruginous Hawks at the home-range scale; and (2) to identify areas of high and low potential conflict with current and future wind-energy developments, by overlaying predictive habitat maps with wind potential within the Canadian Ferruginous Hawk range. We showed that landscape composition and configuration, current industrial development, soil characteristics, and seasonal climate influenced Ferruginous Hawk home-range habitat selection. Our risk analyses identified areas at medium to very high risk of conflict with wind energy, but also large areas with low wind-energy development potential and high conservation value that would be valuable for species conservation and management. Importantly, how wind potential is measured has a strong influence on the level of risk. Our habitat model and risk assessment do not replace ground assessments, but can be used during the pre-development phase to proactively site new wind farms away from potential risk for Ferruginous Hawks.
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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 | 0.001 | 0.001 |
| 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".