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Record W2111590379 · doi:10.1093/beheco/aru060

Factors affecting low resident male siring success in one-male groups of blue monkeys

2014· article· en· W2111590379 on OpenAlexaff
Su‐Jen Roberts, Eleni Nikitopoulos, Marina Cords

Bibliographic record

VenueBehavioral Ecology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsNew York Institute of Technology
Fundersnot available
KeywordsOffspringBiologySireMatingDemographyReproductive successEstrous cycleZoologyGeneticsAnimal sciencePopulationPregnancy

Abstract

fetched live from OpenAlex

In species that live in one-male/multi-female groups, resident males have more access to females than do bachelor males and should have a within-group reproductive advantage. We used a genetic analysis of 13 microsatellite loci to assign paternity to 111 offspring born over 10 years in 8 groups of wild blue monkeys. Resident males sired a maximum of 61% of the offspring conceived in their groups, indicating that despite their greater access to females, residents lost a substantial number of offspring to outsiders. A resident was less likely to sire an offspring when multiple females were in conceptive estrus, suggesting that it is difficult to monitor many fertile females simultaneously. Moreover, multiple estrous females likely attract competitor males, whose presence also decreased the probability that a resident sired an offspring. The negative effect of intruders on resident siring success may occur because females prefer competitors or because an increase in the number of intruders increases the challenge of effective mate guarding by a resident, leading him to miss rare mating opportunities. Tenure length did not affect resident siring success. Identifying the factors affecting patterns of paternity within species will help us to better understand the considerable variation in resident male siring success that occurs in one-male groups.

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 score0.216
Threshold uncertainty score0.809

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 head0.055
GPT teacher head0.276
Teacher spread0.221 · 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

Citations33
Published2014
Admission routes1
Has abstractyes

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