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Record W7128644899 · doi:10.26108/dn0e-cq92

Migratory Connectivity at High Latitudes: Sabine's Gulls (Xema sabini) from a Colony in the Canadian High Arctic Migrate to Different Oceans

2016· article· en· W7128644899 on OpenAlexaboutno aff
Shanti E. Davis, Mark Maftei, Mark L. Mallory, Claudia Mettke-Hofmann

Bibliographic record

VenueAcadiaU-DEV · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsCircumpolar starArcticHabitatRange (aeronautics)SternaThe arcticLatitude

Abstract

fetched live from OpenAlex

The world's Arctic latitudes are some of the most recently colonized by birds, and an understanding of the migratory connectivity of circumpolar species offers insights into the mechanisms of range expansion and speciation. Migratory divides exist for many birds, however for many taxa it is unclear where such boundaries lie, and to what extent these affect the connectivity of species breeding across their ranges. Sabine’s gulls (Xema sabini) have a patchy, circumpolar breeding distribution and overwinter in two ecologically similar areas in different ocean basins: the Humboldt Current off the coast of Peru in the Pacific, and the Benguela Current off the coasts of South Africa and Namibia in the Atlantic. We used geolocators to track Sabine’s gulls breeding at a colony in the Canadian High Arctic to determine their migratory pathways and wintering sites. Our study provides evidence that birds from this breeding site disperse to both the Pacific and Atlantic oceans during the non-breeding season, which suggests that a migratory divide for this species exists in the Nearctic. Remarkably, members of one mated pair wintered in opposite oceans. Our results ultimately suggest that colonization of favorable breeding habitat may be one of the strongest drivers of range expansion in the High Arctic.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.003

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.

Opus teacher head0.011
GPT teacher head0.218
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

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