Migratory and winter movements of Arctic Alaska breeding Sabine’s Gulls (Xema sabini)
Bibliographic record
Abstract
The Sabine’s Gull (Xema sabini) is a pelagic, Arctic-breeding species with a circumpolar breeding distribution. Little is known about migration routes for Sabine’s Gulls breeding in the Alaskan Arctic. We tagged Sabine’s Gulls on their northern Alaska breeding grounds to identify migration routes and wintering areas and compare geolocators and GPS pinpoint tags for use on small-bodied gulls. Twelve geolocators were deployed in northern Alaska in 2011 (Colville River Delta) of which four were recovered, and five GPS pinpoint tags in 2021 (Qupaluk). Although the GPS pinpoint tags provided more accurate locations allowing for finer-scale habitat evaluation, and did not require recapture of birds, the overall coverage provided by geolocators was superior in this study given the constraints of the number of locations GPS pinpoint tags can record. Broadly, the four (one tag failed) tracked Sabine’s Gulls migrated away from the breeding grounds as expected, passing along the west coast of Alaska and south along the west coast of the Americas to winter in the Humboldt Current off the coast of Peru. Our tracked gulls used the same migratory staging and wintering areas as did Sabine’s Gulls breeding in the Canadian Arctic (Davis et al., 2016). Such reliance on specific marine areas presents risks from climate-related changes or ecological damage to those areas.
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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 | 0.000 |
| 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) | 0.001 | 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".