Nomadic breeders Snowy Owls (<i>Bubo scandiacus</i>) do not use stopovers to sample the summer environment
Why this work is in the frame
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Bibliographic record
Abstract
Whereas most migratory animals, such as many birds of prey, return to the same breeding area each summer, nomadic breeders search over large distances to locate breeding areas that vary greatly in location from year to year. Nomadic breeders are assumed to extensively sample patch quality before selecting a summer settlement site (e.g. breeding site) with a high abundance of prey. In addition, patch selection during migration might vary, with immature birds sampling the summer environment for the first time. Here, we examined the migratory movements of a nomadic breeder, the Snowy Owl, to determine whether there are differences in phenology among age and sex classes, and where stopovers occur along their migratory journey. Each owl ( n = 24) was equipped with a GPS‐GSM transmitter during the overwintering period in the USA and Canada from 2014 to 2018. Movement patterns followed a two‐process Poisson distribution, allowing us to separate stopovers from directional flights (i.e. migration). Adults completed migration earlier than immatures, with no difference in number of stopovers or time spent at each stopover. Snowy Owls had a higher probability of having a stopover at the beginning of their migration than at the end. Moreover, stopovers occurred primarily on frozen waterbodies more suitable for foraging or roosting outside of the summer range. We conclude that Snowy Owls use stopovers primarily to build up reserves or to rest during migration and they can potentially select appropriate summer settlement sites via short overflights without extensive sampling of patches during lengthy stopovers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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 it