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Record W2069142717 · doi:10.1098/rspb.2014.0844

Do avian cooperative breeders live longer?

2014· article· en· W2069142717 on OpenAlexaff
Guy Beauchamp

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCooperative breedingAvian clutch sizeBiologyClutchEconomic shortageSample size determinationPopulationSample (material)LongevityEcologyDemographyReproductionStatisticsEngineeringMathematicsGenetics

Abstract

fetched live from OpenAlex

Cooperative breeding is not common in birds but intriguingly over-represented in several families, suggesting that predisposing factors, similar ecological constraints or a combination of the two facilitate the evolution of this breeding strategy. The life-history hypothesis proposes that cooperative breeding is facilitated by high annual survival, which increases the local population and leads to a shortage of breeding opportunities. Clutch size in cooperative breeders is also expected to be smaller. An earlier comparative analysis in a small sample of birds supported the hypothesis but this conclusion has been controversial. Here, I extend the analysis to a larger, worldwide sample and take into account potential confounding factors that may affect estimates of a slow pace of life and clutch size. In a sample of 81 species pairs consisting of closely related cooperative and non-cooperative breeders, I did not find an association between maximum longevity and cooperative breeding, controlling for diet, body mass and sampling effort. However, in a smaller sample of 37 pairs, adult annual survival was indeed higher in the cooperative breeders, controlling for body mass. There was no association between clutch size and cooperative breeding in a sample of 93 pairs. The results support the facilitating effect of high annual survival on the evolution of cooperative breeding in birds but the effect on clutch size remains elusive.

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)0.001
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 score0.352
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.032
GPT teacher head0.235
Teacher spread0.203 · 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

Citations37
Published2014
Admission routes1
Has abstractyes

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