Nomadic breeders Snowy Owls (<i>Bubo scandiacus</i>) do not use stopovers to sample the summer environment
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.002 | 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".