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Record W2099086097 · doi:10.1675/063.038.0209

Patterns of Molt in Long-Tailed Ducks (<i>Clangula hyemalis</i>) during Autumn and Winter in the Great Lakes Region, Canada

2015· article· en· W2099086097 on OpenAlexafffundabout
Andreanne M. Payne, Michael L. Schummer, Scott A. Petrie

Bibliographic record

VenueWaterbirds · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsBirds CanadaWestern University
FundersOntario Federation of Anglers and Hunters
KeywordsWaterfowlBiologyReproductionEcologyNutrientArcticAnnual cycleOverwinteringFledgeZoologyPredationHabitat

Abstract

fetched live from OpenAlex

Molt and migration can coincide in Arctic nesting waterfowl because they have little time between fledging and the severe weather that precipitates migration. Objectives were to observe how patterns in autumn and winter molt by Long-tailed Ducks (Clangula hyemalis) were influenced by nutrient reserves or seasonal life-cycle events. Molt scores and nutrient reserves were determined for birds salvaged during autumn 2011 (n = 79) and collected during winter 2002–2004 (n = 255). Differences in molt among sex-age classes and correlation between molt and nutrient reserves were determined. It was predicted that adult females and juveniles of both sexes suspended molt during autumn migration to limit energetic overlap; however, greater molt in juveniles during autumn than winter was detected. Correlation between molt and nutrient reserves were not detected. Molt was less in adult males than females and juveniles during winter, which may suggest that the effects of reproduction (females) and growth (juveniles) extended their molt into winter. Observed molt patterns are consistent with fixed cues associated with the timing of seasonal life-cycle events of this species. This could have important implications in understanding the life-cycle events of Long-tailed Ducks and provide novel explanations of seasonal molt.

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 score0.786
Threshold uncertainty score0.898

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 head0.011
GPT teacher head0.208
Teacher spread0.197 · 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

Citations1
Published2015
Admission routes3
Has abstractyes

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