Widespread Lead Exposure in Golden Eagles Captured in Montana
Bibliographic record
Abstract
ABSTRACT Lead poisoning threatens many species of raptors, including golden eagles ( Aquila chrysaetos ). Much of this lead likely comes from bullet fragments that remain in the carcasses of animals killed by hunters. The likelihood of lead exposure may peak during fall hunting seasons and early winter until carcasses from hunting become scarce. From 2011 to 2018 in western Montana, USA, we captured 91 golden eagles in winter, tested their blood lead levels (BLL), and outfitted a subset of birds ( n = 29) with global positioning system [GPS] transmitters. Nearly all golden eagles (94.5%) had elevated BLL (≥10 μg/dL), and 8 of them had BLL above clinical exposure (>60 μg/dL), where they may lose coordination and experience a host of other neurological and physiological disorders. Golden eagles caught late in winter tended to have lower BLL than those caught earlier. At least 69% of the golden eagles equipped with GPS transmitters migrated northward, spending the summer throughout Alaska, USA, and northwestern Canada. Blood lead levels did not differ between migratory and nonmigratory golden eagles. Overall, elevated BLL are widespread among golden eagles throughout winter in western Montana. Promoting nonlead hunting ammunition in areas with high densities of golden eagles will reduce the birds' lead exposure. © 2020 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".