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Record W3208365614 · doi:10.1073/pnas.2107881118

Fevers and the social costs of acute infection in wild vervet monkeys

2021· article· en· W3208365614 on OpenAlexafffund
Richard McFarland, S. Peter Henzi, Louise Barrett, Tyler R. Bonnell, Andrea Fuller, Christopher Young, Robyn S. Hetem

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2021
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsNational Research FoundationClaude Leon FoundationUniversity of the Witwatersrand, JohannesburgGovernment of CanadaCarnegie Corporation of New York
KeywordsLethargyAgonistic behaviourAggressionPrimateSickness behaviorAnorexiaBiologyDiseaseZoologyMedicineImmunologyEcologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

) with miniature data loggers to obtain continuous measurements of core body temperature. We detected 128 fevers in 43 monkeys, totaling 776 fever-days over a 6-year period. Fevers were characterized by a persistent elevation in mean and minimum 24-h body temperature of at least 0.5 °C. Corresponding behavioral data indicated that febrile monkeys spent more time resting and less time feeding, consistent with the known sickness behaviors of lethargy and anorexia, respectively. We found no evidence that fevers influenced the time individuals spent socializing with conspecifics, suggesting social transmission of infection within a group is likely. Notably, febrile monkeys were targeted with twice as much aggression from their conspecifics and were six times more likely to become injured compared to afebrile monkeys. Our results suggest that sickness behavior, together with its agonistic consequences, can carry meaningful costs for highly gregarious mammals. The degree to which social factors modulate the welfare of infected animals is an important aspect to consider when attempting to understand the ecological implications of disease.

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 score0.447
Threshold uncertainty score0.371

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.049
GPT teacher head0.374
Teacher spread0.325 · 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

Citations25
Published2021
Admission routes2
Has abstractyes

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Same venueProceedings of the National Academy of SciencesSame topicPrimate Behavior and EcologyFrench-language works237,207