First evidence of diverging migration and overwintering strategies in glaucous gulls (<i>Larus hyperboreus</i>) from the Canadian Arctic
Bibliographic record
Abstract
Abstract Many seabird populations differ in their migration strategies, where individuals travel in different directions to separate wintering areas. These migratory strategies may expose individuals to different threats, thus understanding migratory connectivity is crucial to assess risks to populations. Glaucous gulls (Larus hyperboreus) are generalist predators with flexible migratory behaviour that may alter these behaviours in response to climate change and anthropogenic activities, such as access to landfills, yet little is known about their migration. We deployed GPS and GLS tracking devices on glaucous gulls from Coats Island, Nunavut, Canada to obtain the first insights into their migration and habitat use outside of the breeding season. Gulls used two migration strategies during the non-breeding season, where one migrated as far as the Sea of Okhotsk in the Pacific and the remainder (n = 7) wintered in the North Atlantic. Gulls primarily overwintered in pelagic (56%) and coastal (38%) habitats. While in coastal habitats, one gull visited one landfill once, but visits increased with a 1 km and 3 km buffer, suggesting that urban glaucous gulls primarily used non-landfill habitats. This research can be used as a baseline to explore changes in migratory behaviour and inform future conservation of Arctic-breeding gulls.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".