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Record W2513376211 · doi:10.1642/auk-16-60.1

Body condition in Snowy Owls wintering on the prairies is greater in females and older individuals and may contribute to sex-biased mortality

2016· article· en· W2513376211 on OpenAlexaffabout
Alexander M. Chang, Karen L. Wiebe

Bibliographic record

VenueThe Auk · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsDominance (genetics)Sexual dimorphismBiologyDemographySeasonal breederEcologyZoology

Abstract

fetched live from OpenAlex

Birds that winter in cold northern climates experience harsh conditions, including reduced food availability and increased energy demands. In raptors, the ability to forage and maintain body condition may be related to age (hunting experience) or the ability to defend good-quality territories (dominance). We examined the effects of age and sex on body condition and various sources of mortality in wintering Snowy Owls (Bubo scandiacus) on the Canadian prairies. Because of reversed sexual size dimorphism, we predicted that females, the dominant sex, would be in better condition than males and that adults would be in better condition than juveniles. Consistent with these predictions, data from 537 live Snowy Owls trapped over 18 winter field seasons showed that adults were heavier than juveniles for a given body size and carried more fat reserves. We found that 56% of males lacked furcular and wing-pit fat, whereas only 31% of females lacked such fat; and females, but not males, tended to put on fat during the winter months. A comparison of the sex ratio of starving Snowy Owls turned in to rehabilitation centers (63% male) and that of living Snowy Owls observed in the wild (45% male) showed a male bias in starving and diseased individuals. Although most of the wild-trapped birds were above the starvation threshold, the proximate mechanisms by which sex-biased competitive dominance is manifested in differences in body condition and survival warrant further study.

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 score0.007
Threshold uncertainty score0.759

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)0.0010.000

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 head0.031
GPT teacher head0.288
Teacher spread0.257 · 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

Citations46
Published2016
Admission routes2
Has abstractyes

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