Biotelemetry data for Golden Eagles (Aquila chrysaetos) captured in coastal southern California, February 2017–December 2019
Bibliographic record
Abstract
First posted July 28, 2020 For additional information, contact: Director,Western Ecological Research CenterU.S. Geological Survey3020 State University Drive EastSacramento, California 95819 Because of a lack of clarity about the status of golden eagles (Aquila chrysaetos) in coastal southern California, the U.S. Geological Survey, in collaboration with U.S. Fish and Wildlife Service, California Department of Fish and Wildlife, Bureau of Land Management, and San Diego Management and Monitoring Program, began a multi-year survey and tracking program of golden eagles to address questions regarding habitat use, movement behavior, nest occupancy, genetic population structure, and human impacts on eagles. Golden eagle trapping and tracking efforts began in September 2014. During trapping efforts from September 29, 2014, to February 23, 2017, 37 golden eagles were captured. During trapping efforts from February 24, 2017, to December 2, 2019, an additional 7 golden eagles (4 females and 3 males) were captured, and one previously captured female was recaptured in San Diego County. Biotelemetry data for 27 of the 44 golden eagles that were transmitting data from February 24, 2017, to December 2, 2019, are presented. These eagles ranged as far north as British Columbia, Canada, and as far south as Ciudad Insurgentes, Baja California, Mexico.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.002 |
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".