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Record W2159514208 · doi:10.14430/arctic4

The Annual Migration Cycle of Emperor Geese in Western Alaska

2009· article· en· W2159514208 on OpenAlexvenueaboutno aff
Jerry W. Hupp, Joel A. Schmutz, Craig R. Ely

Bibliographic record

VenueARCTIC · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsPeninsulaEmperorGeographyShoreDeltaPhysical geographyOceanographyEcologyGeologyArchaeologyBiology

Abstract

fetched live from OpenAlex

Most emperor geese (Chen canagica) nest in a narrow coastal region of the Yukon-Kuskokwim Delta (YKD) in western Alaska, but their winter distribution extends more than 3000 km from Kodiak Island, Alaska, to the Commander Islands, Russia. We marked 53 adult female emperor geese with satellite transmitters on the YKD in 1999, 2002, and 2003 to examine whether chronology of migration or use of seasonal habitats differed among birds that wintered in different regions. Females that migrated relatively short distances (650–1010 km) between the YKD and winter sites on the south side of the Alaska Peninsula bypassed autumn staging areas on the Bering Sea coast of the Alaska Peninsula or used them for shorter periods (mean = 57 days) than birds that made longer migrations (1600–2640 km) to the western Aleutian Islands (mean = 97 days). Alaska Peninsula migrants spent more days at winter sites (mean = 172 days, 95% CI: 129–214 days) than western Aleutian Island migrants (mean = 91 days, 95% CI: 83–99 days). Birds that migrated 930–1610 km to the eastern Aleutian Islands spent intermediate intervals at fall staging (mean = 77 days) and wintering areas (mean = 108 days, 95% CI: 95–119 days). Return dates to the YKD did not differ among birds that wintered in different regions. Coastal staging areas on the Alaska Peninsula may be especially important in autumn to prepare Aleutian migrants physiologically for long-distance migration to winter sites, and in spring to enable emperor geese that migrate different distances to reach comparable levels of condition before nesting.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.277

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.235
Teacher spread0.229 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations15
Published2009
Admission routes2
Has abstractyes

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Same venueARCTICSame topicAvian ecology and behaviorFrench-language works237,207