Limitations and mechanisms influencing the migratory performance of soaring birds
Bibliographic record
Abstract
Migration is costly in terms of time, energy and safety. Optimal migration theory suggests that individual migratory birds will choose between these three costs depending on their motivation and available resources. To test hypotheses about use of migratory strategies by large soaring birds, we used GPS telemetry to track 18 adult, 13 sub‐adult and 15 juvenile Golden EaglesAquila chrysaetosin eastern North America. Each age‐class had potentially different motivations during migration. During spring, the migratory performance (defined here as the directness of migratory flight) of adults was higher than that of any other age‐classes. Adults also departed earlier and spent less time migrating. Together, these patterns suggest that adults were primarily time‐limited and the other two age‐classes were energy‐limited. However, adults that migrated the longest distances during spring also appeared to take advantage of energy‐conservation strategies such as decreasing their compensation for wind drift. During autumn, birds of all age‐classes were primarily energy‐minimizers; they increased the length of stopovers, flew less direct routes and migrated at a slower pace than during spring. Nonetheless, birds that departed later in autumn flew more directly, indicating that time limitations may have affected their decision‐making. During both seasons, juveniles had the lowest performance, sub‐adults intermediate performance and adults the highest performance. Our results show age‐ and seasonal variation in time and energy‐minimization strategies that are not necessarily exclusive of one another. Beyond time and energy, a complex suite of factors, including weather, experience and navigation ability, influences migratory performance and decision‐making.
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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 | 0.000 | 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) | 0.001 | 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".