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Evidence for Wing Molt and Breeding Site Fidelity in King Eiders

2006· article· en· W2209863083 on OpenAlexaboutno aff
Laura M. Phillips, Abby N. Powell

Bibliographic record

VenueWaterbirds · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsWingPhilopatrySeasonal breederBiological dispersalFledgeGeographyFisheryPredationBiologyEcologyDemography

Abstract

fetched live from OpenAlex

Fidelity of King Eiders (Somateria spectabilis) to breeding and wing molt sites was examined using satellite telemetry data obtained opportunistically when battery life of transmitters provided locations in a second year. Consecutive breeding locations were obtained for eleven female and 23 male King Eiders. All females exhibited breeding site fidelity by returning to sites within 15 km of first year breeding areas on the North Slope of Alaska. Breeding locations of males in a subsequent year were located on average >1000 km from their prior breeding sites and were primarily outside Alaska, on the coasts of Russia and Canada. Second-year wing molt locations were obtained for two female and six male King Eiders. Wing molt sites of males were located 6.2 ± 3.1 km apart on average in successive years, while female wing molt locations averaged almost 50 km apart. Our results demonstrate site fidelity of female King Eiders to a breeding area on the North Slope of Alaska, document the dispersal of male King Eiders between breeding seasons, and present the first evidence for wing molt site fidelity in males.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.275
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), 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

Citations43
Published2006
Admission routes1
Has abstractyes

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