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Record W2093097287 · doi:10.1111/1365-2435.12313

Daily energy expenditure during lactation is strongly selected in a free‐living mammal

2014· article· en· W2093097287 on OpenAlexafffund
Quinn E. Fletcher, John R. Speakman, Stan Boutin, Jeffrey E. Lane, Andrew G. McAdam, Jamieson C. Gorrell, David W. Coltman, Murray M. Humphries

Bibliographic record

VenueFunctional Ecology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsUniversity of GuelphUniversity of AlbertaMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsBiologyMammalLactationEnergy expenditureEcologyEnergy metabolismMarine mammalEnergeticsZoologyAnimal scienceEndocrinologyPregnancy

Abstract

fetched live from OpenAlex

Summary Energy expenditure is a trait of central importance in ecological and evolutionary theory. We examined the correlates of, the strength of selection on, and the heritability of, daily energy expenditure ( DEE ; kJ day −1 ) during lactation in free‐ranging North American red squirrels ( T amiasciurus hudsonicus ). Over 7 years, lactating squirrels with greater DEE had higher annual reproductive success ( ARS ; standardized selection gradient: β′ = 0·47; top 12% of published estimates). Surprisingly, positive fecundity selection on lactation DEE for increased ARS did not result because lactation DEE was correlated with typical measures of reproductive performance and/or investment. We found no evidence of costs of elevated lactation DEE acting through female survival, subsequent year lactation DEE or subsequent year reproduction. Lactation DEE was not significantly repeatable, and heritability was not significantly different from zero. Elevated lactation DEE enhances ARS through a link between DEE and an unidentified measure of maternal or environmental quality, but there is limited evolutionary potential for lactation DEE to respond to our documented selection.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.998

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)0.0020.000

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 head0.009
GPT teacher head0.176
Teacher spread0.168 · 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.

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

Citations21
Published2014
Admission routes2
Has abstractyes

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