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Record W2115103290 · doi:10.1139/cjz-2013-0122

Do social mating systems limit maternal immune investment in shorebirds?

2014· article· en· W2115103290 on OpenAlexafffundvenue
Darryl B. Edwards, Monika M. M. Haring, H. Grant Gilchrist, Albrecht I. Schulte‐Hostedde

Bibliographic record

VenueCanadian Journal of Zoology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsCarleton UniversityEnvironment and Climate Change CanadaLaurentian University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyPolygynyOffspringMating systemMatingSexual selectionProvisioningParental investmentEcologyInvestment (military)Paternal careZoologyPromiscuityDemographyPregnancyGeneticsPopulation

Abstract

fetched live from OpenAlex

Across mating systems, females differ in the amount of resources they invest in offspring. For example, polyandrous females invest in acquiring multiple matings rather than providing parental care. We examined how the amount of maternal immune investment, measured as immunoglobulin Y and lysozyme activity in eggs, was influenced by female role across three social mating systems (polyandry, polygyny, and monogamy) in shorebirds. We predicted that polyandry should impose the greatest costs on the ability to provision eggs and monogamy, where females receive benefits from biparentality, the least. Contrary to our predictions, levels of maternally derived egg immune constituents were consistently high across measures in the polyandrous species and low in the monogamous species. Our results may support a link with pace-of-life where developmental costs are greater than the energetic costs of provisioning eggs, and (or) a role for sexual selection acting on maternal immune investment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.024
GPT teacher head0.215
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2014
Admission routes3
Has abstractyes

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