Evaluation of Wind‐Energy Survey Protocols for Migrating Eagle Detection
Bibliographic record
Abstract
ABSTRACT Wind energy development is increasing in the United States and Canada and may affect bald ( Haliaeetus leucocephalus ) and golden eagle ( Aquila chrysaetos ) populations through direct mortality. Wind farms on ridge‐tops may present a greater mortality threat because of the importance of these features for migrating raptors. Regulators in both countries recommend methods for preconstruction surveys, so that eagle use and collision risk can be assessed at potential construction sites. We obtained hourly count data collected during the autumn seasons of 1990–2014 from 22 ridge‐top raptor‐migration monitoring sites in the Pacific, Eastern, and Central flyways of North America. We simulated 18 different survey protocols and effort levels based on survey guidance by repeatedly drawing quasi‐random subsamples from continuously collected data in a manner that imitated the recommended protocols, comparing the number of eagles seen in simulated counts to the eagles seen in all data collected to assess the effectiveness of those protocols in correctly estimating the eagle passage rate (eagles detected per hour of counts) at each site. Multihour point counts conducted on a weekly basis were found to be ineffective at correctly estimating eagle passage both within and across years. Counts conducted in the afternoon were found to be more effective than those conducted in the morning. We demonstrate that full‐day counts, conducted weekly during the peak period of eagle passage, were much more effective at estimating eagle passage rate. Although current guidance recommends the performance of such full day counts, this result underscores their importance in preconstruction surveys at potential wind sites as an essential tactic to estimate eagle passage and risk of collision with energy infrastructure. © 2018 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 | 0.037 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".