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Record W3087789613 · doi:10.1038/s41598-020-72376-x

Parasite intensity drives fetal development and sex allocation in a wild ungulate

2020· article· en· W3087789613 on OpenAlexafffund
O. Alejandro Aleuy, Emmanuel Serrano, Kathreen E. Ruckstuhl, Eric P. Hoberg, Susan Kutz

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaKillam TrustsNational Science Foundation
KeywordsUngulateParasite hostingBiologyZoologyEcologyDemographyComputer scienceWorld Wide WebHabitat

Abstract

fetched live from OpenAlex

An understanding of the mechanisms influencing prenatal characteristics is fundamental to comprehend the role of ecological and evolutionary processes behind survival and reproductive success in animals. Although the negative influence of parasites on host fitness is undisputable, we know very little about how parasitic infection in reproductive females might influence prenatal factors such as fetal development and sex allocation. Using an archival collection of Dall's sheep (Ovis dalli dalli), a capital breeder that depends on its body reserves to overcome the arctic winter, we investigated the direct and indirect impacts of the parasite community on fetal development and sex allocation. Using partial least squares modelling, we observed a negative effect of parasite community on fetal development, driven primarily by the nematode Marshallagia marshalli. Principal component analysis demonstrated that mothers with low parasite burden and in good body condition were more likely to have female versus male fetuses. This association was primarily driven by the indirect effect of M. marshalli on ewe body condition. Refining our knowledge of the direct and indirect impact that parasite communities can have on reproduction in mammals is critical for understanding the effects of infectious diseases on wildlife populations. This can be particularly relevant for species living in ecosystems sensitive to the effects of global climate change.

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 score0.179
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

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

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 head0.039
GPT teacher head0.284
Teacher spread0.245 · 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

Citations15
Published2020
Admission routes2
Has abstractyes

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