Inter-individual variation in the migratory behaviour of a generalist seabird, the herring gull (<i>Larus smithsoniansus</i>), from the Canadian Arctic
Bibliographic record
Abstract
Abstract The Arctic is warming three times faster than the rest of the globe, causing rapid transformational changes in Arctic ecosystems. As these changes increase, understanding seabird movements will be important for predicting how they respond to climate change, and thus how we plan for conservation. Moreover, as most Arctic-breeding seabirds only spend the breeding season in the Arctic, climate change may also affect them through habitat changes in their non-breeding range. We used Global Location Sensors (GLS) to provide new insights on the movement of Arctic-breeding herring gulls ( Larus smithsoniansus ) in North America. We tracked gulls that wintered in the Gulf of Mexico ( n = 7) or the Great Lakes ( n = 1), and found that migratory routes and stopover sites varied between individuals, and between southbound and northbound migration. This inter-individual variation suggests that herring gulls, as a generalist species, can make use of an array of regions during migration, but may be more susceptible to climate change impacts in their overwintering locations than during migration. However, due to our limited sample size, future, multi-year studies are recommended to better understand the impacts of climate change on this Arctic-breeding seabird.
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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 | 0.001 | 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) | 0.001 | 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".