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Record W3141537205 · doi:10.1675/063.043.0203

Spatial Ecology of White-Winged Scoters (Melanitta deglandi) in Eastern North America: A Multi-Year Perspective

2020· article· en· W3141537205 on OpenAlexaffabout
Christine Lepage, Jean‐Pierre L. Savard, Scott G. Gilliland

Bibliographic record

VenueWaterbirds · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsCapeGeographyEcologyBorealEstuarySpring (device)White (mutation)TaigaFisheryBiologyArchaeology

Abstract

fetched live from OpenAlex

Satellite transmitters were used to describe migration patterns and establish connectivity among breeding and wintering areas for 30 White-winged Scoters (Melanitta deglandi) tagged in August 2010 or August 2012 during remigial molt in the St. Lawrence Estuary, Quebec, Canada. Fourteen potential breeding sites were identified in the boreal forest from Quebec to the Northwest Territories, Canada. Most birds molted at marine sites, except for two females that molted close to their breeding areas and a male that molted in interior Manitoba. Most birds remained near their molting location during fall prior to migrating to their wintering area. Individuals tended to use similar fall migration routes from year to year. Most birds (81%) wintered in the Long Island-Nantucket-Cape Cod region along the eastern seaboard of the USA, while only three birds wintered in Canada. Scoters spent almost half the year on wintering areas, and 83% returned within 150 km of the previous year's site. Spring migration patterns depended on breeding status. Breeding birds covered an average of 6,880 km compared to 2,550 km by non-breeding birds during their annual cycle. The St. Lawrence Estuary in Quebec and the Long Island-Nantucket-Cape Cod region (New York state and Massachusetts) were areas particularly important for tagged birds.

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)0.000
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 score0.564
Threshold uncertainty score0.877

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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