Estimating updraft velocity components over large spatial scales: contrasting migration strategies of golden eagles and turkey vultures
Bibliographic record
Abstract
Soaring birds migrate in massive numbers worldwide. These migrations are complex and dynamic phenomena, strongly influenced by meteorological conditions that produce thermal and orographic uplift as the birds traverse the landscape. Herein we report on how methods were developed to estimate the strength of thermal and orographic uplift using publicly available digital weather and topography datasets at continental scale. We apply these methods to contrast flight strategies of two morphologically similar but behaviourally different species: golden eagle, Aquila chrysaetos, and turkey vulture, Cathartes aura, during autumn migration across eastern North America tracked using GPS tags. We show that turkey vultures nearly exclusively used thermal lift, whereas golden eagles primarily use orographic lift during migration. It has not been shown previously that migration tracks are affected by species-specific specialisation to a particular uplift mode. The methods introduced herein to estimate uplift components and test for differences in weather use can be applied to study movement of any soaring species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".