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South American sea lions in Peru have a lek‐like mating system

2010· article· en· W1996408124 on OpenAlexafffund
Karim H. Soto, Andrew W. Trites

Bibliographic record

VenueMarine Mammal Science · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolygynyMatingSeasonal breederMating systemEcologyTemperate climateSex ratioBiologyRange (aeronautics)SubtropicsLek matingGeographyZoologyDemographyMate choicePopulation

Abstract

fetched live from OpenAlex

Abstract Five years of behavioral observations revealed significant effects of high air temperatures and breeding site topography on the mating system of South American sea lions in Peru. Unlike most polygynous mammals that defend females or fixed territories, male sea lions in Peru maintained positions along the shoreline where females passed each day to thermoregulate, and where most copulations occurred. Sex ratios (1 male per 17 females) and male mating success were extremely skewed (14% of males achieved 50% of the copulations, and 25% of them did not copulate at all). The mass daily movements of females toward the water and cool substrate of the shoreline, along with a highly skewed sex ratio, accentuated the difficulty for males to monopolize and restrict female movements. Females moved freely and chose their mates, unlike in temperate regions of their range where male South American sea lions control groups of females or access to tide pools. Our observations indicate that the South American sea lion in Peru has a lek‐like breeding system. This is a rare alternative to the common male strategies of defending females and resources, and is likely an evolutionary product of their highly skewed sex ratio, protracted breeding season, and the extreme subtropical climate where they breed.

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 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 score0.218
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.229
Teacher spread0.220 · 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
Published2010
Admission routes2
Has abstractyes

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