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Record W4317503517 · doi:10.1002/ajpa.24689

Maternal social position and survival to weaning in arid‐country vervet monkeys

2023· article· en· W4317503517 on OpenAlexafffund
Rosemary Blersch, Tyler R. Bonnell, Madison Clarke, Marcus J. Dostie, Miranda Lucas, Jonathan Jarrett, Richard McFarland, Christina Nord, April Takahashi, Stephanie Varsanyi, Chloé Vilette, Christopher Young, Louise Barrett, S. Peter Henzi

Bibliographic record

VenueAmerican Journal of Biological Anthropology · 2023
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of CanadaNational Research Foundation
KeywordsVervet monkeyDemographyWeaningPopulationSurvivorship curveInfant mortalityBiologyGeographyZoologyAnimal scienceSociology

Abstract

fetched live from OpenAlex

Abstract Objectives We consider the relative contributions of maternal rank and sociability to the survival of infant vervet monkeys ( Chlorocebus pygerythrus ) to nutritional independence (~210 days) in a generally resource‐poor environment. Methods We analyzed survival data from 153 infants born to 60 mothers across three free‐ranging troops and 10 yearly birth cohorts at a South African research site experiencing general but variable resource scarcity. Results The population was characterized by a pre‐weaning mortality of 30% (Range: 9%–85%), with a median age at death of 50 days. In addition to the consequences of resource availability, increased infant survival was independently and equivalently positively associated with higher maternal rank and a greater number of maternal spatial partners. Discussion We use this outcome to suggest that apparent discrepancies across sites and species in the relative importance of different maternal attributes in determining reproductive outcomes may be resolved by considering more closely local sources of infant mortality.

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 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 score0.050
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.042
GPT teacher head0.376
Teacher spread0.335 · 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

Citations14
Published2023
Admission routes2
Has abstractyes

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