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Record W2822943769 · doi:10.1093/cz/zoy050

Refuge size variation and potential for sperm competition in Wellington tree weta

2018· article· en· W2822943769 on OpenAlexafffund
Tina W. Wey, Clint D. Kelly

Bibliographic record

VenueCurrent Zoology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSperm competitionBiologySexual selectionCompetition (biology)MatingSpermMating systemEcologyOperational sex ratioZoologyReproductive successMate choiceDemographyBotanyPopulation

Abstract

fetched live from OpenAlex

, plural "weta"), the size of tree cavities (called galleries) used as refuges affects weta distribution and thus the opportunity for sexual selection and selection on male weaponry size. We examined the predicted effects of gallery size and male weaponry size on the potential for sperm competition. We asked if gallery size influenced the potential for multiple mating by females and potential for sperm competition, if male weaponry size was associated with relative expected sperm competition intensity (SCI), and if estimated male mating success was correlated with potential SCI. To quantify relative competitive environments of males, we created and analyzed networks of potential competitors based on which males could have mated with the same females. We found that small galleries had higher potential for female multiple mating and higher potential for sperm competition. Size of male weaponry was not associated with expected relative SCI. Regardless of gallery size, males with more potential mates were expected to face lower expected relative sperm competition. Thus, in this system, variation in the size of available refuges is likely to influence the potential for sperm competition, in a way that we might expect to increase variation in overall reproductive success.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.104

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.038
GPT teacher head0.246
Teacher spread0.209 · 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

Citations9
Published2018
Admission routes2
Has abstractyes

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