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The interaction between personality, offspring fitness and food abundance in North American red squirrels

2007· article· en· W2097970230 on OpenAlexaff
Adrienne K. Boon, Denis Réale, Stan Boutin

Bibliographic record

VenueEcology Letters · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversité du Québec à MontréalUniversity of Alberta
Fundersnot available
KeywordsPersonalityBiologyNest (protein structural motif)OffspringEcologyReproductive successPopulationSelection (genetic algorithm)LitterAbundance (ecology)Big Five personality traitsLife history theoryHeritabilityZoologyDemographyLife historyPsychologyEvolutionary biologySocial psychology

Abstract

fetched live from OpenAlex

Animal personality is now frequently reported in wild and captive populations. It has been shown to be moderately heritable and to have potentially important fitness consequences. Variation in personality within a population may be maintained by balancing selection if different values of personality traits are favoured under different conditions. We measured personality in 98 female North American red squirrels (Tamiasciurus hudsonicus Erxleben), and examined whether its variation could be maintained by changing selection pressures acting via reproductive traits and yearly variation in food abundance. There was no effect of personality on parturition date or litter size, but a female's activity was correlated to the growth rate of her offspring in the nest, and her aggressiveness was correlated to their survival in the nest and overwinter. The magnitude and direction of the effects changed among life history stages and years, possibly in association with food supply in some cases, and may indicate a role for balancing selection in the maintenance of personality.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.018
GPT teacher head0.266
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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

Citations291
Published2007
Admission routes1
Has abstractyes

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