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Record W2145344952 · doi:10.1525/cond.2013.120091

Understanding Sex Differences in Parental Effort in a Migratory Songbird: Examining a Sex-Specific Trade-off between Reproduction and Molt

2013· article· en· W2145344952 on OpenAlexaff
Elizabeth A. Gow, Bridget J. M. Stutchbury

Bibliographic record

VenueOrnithological Applications · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversity of SaskatchewanYork University
Fundersnot available
KeywordsSongbirdBiologyPaternal careMoultingDemographyReproductionSeasonal breederMudaEcologyZoologyLarvaPregnancy

Abstract

fetched live from OpenAlex

Migratory birds are expected to experience a trade-off between reproductive effort, the timing and pace of molt, and initiation of fall migration. The purpose of this study was to investigate if males and females of a songbird that migrates to the neotropics, the Wood Thrush (Hylocichla mustelina), experience a trade-off between parental care and the timing of molt. We determined the relative contribution of males and females in feeding young, assessed how reproductive effort influenced timing of molt, and tested the prediction that the sex provisioning nestlings at the higher rate should molt later. Males fed significantly more than females throughout the breeding period, and males with higher feeding rates had greater nesting success. Males compensated for females' reduced provisioning rate at late nests, and females initiated molt earlier than males. We found a significant negative relationship between the timing of molt and the number of young fledged per season. Males that began molt earlier than females and late-molting males also increased their pace of molt. For males, the cost of reduced parental care is likely higher than the benefit of earlier molt, but delayed onset of molt is partially mitigated via a faster pace of feather growth.

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 score0.013
Threshold uncertainty score0.616

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 head0.103
GPT teacher head0.260
Teacher spread0.158 · 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

Citations18
Published2013
Admission routes1
Has abstractyes

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