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Record W4392242363 · doi:10.1093/beheco/arae013

Born with an advantage: early life and maternal effects on fitness in female ground squirrels

2024· article· en· W4392242363 on OpenAlexafffund
Tanner Yuen, Kathreen E. Ruckstuhl, April Robin Martinig, Peter Neuhaus

Bibliographic record

VenueBehavioral Ecology · 2024
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyZoologyEcology

Abstract

fetched live from OpenAlex

Abstract Lifetime fitness and its determinants are an important topic in the study of behavioral ecology and life-history evolution. Early life conditions comprise some of these determinants, warranting further investigation into their impact. In some mammals, babies born lighter tend to have lower life expectancy than those born heavier, and some of these life-history traits are passed on to offspring, with lighter-born females giving birth to lighter offspring. We investigated how weight at weaning, the relative timing of birth in the season, maternal weight, and maternal age affected the longevity and lifetime reproductive success (LRS) of female Columbian ground squirrels (Urocitellus columbianus). We hypothesized that early life conditions such as offspring weight would not only have lifetime fitness consequences but also intergenerational effects. We found that weight at weaning had a significant impact on longevity, with heavier individuals living longer. The relative timing of an individual’s birth did not have a significant association with either longevity or LRS. Individuals born to heavier mothers were found to have significantly higher LRS than those born to lighter mothers. Finally, maternal age was found to be significantly associated with their offspring’s LRS, with older mothers having less successful offspring. Our results provide evidence that early life conditions do have lifelong fitness and sometimes intergenerational consequences for Columbian ground squirrels.

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 score0.009
Threshold uncertainty score0.512

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 head0.029
GPT teacher head0.347
Teacher spread0.318 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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