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Record W2905780393 · doi:10.1139/cjz-2017-0344

Influence of body size and familiarity on mating and reproductive parameters in the zig-zag ladybird beetle, <i>Menochilus sexmaculatus</i> (Coleoptera: Coccinellidae)

2018· article· en· W2905780393 on OpenAlexvenueno aff
Priya Singh, Geetanjali Mishra, Omkar

Bibliographic record

VenueCanadian Journal of Zoology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyCoccinellidaeMatingFecundityZoologyAntagonistic CoevolutionSexual selectionEcologySexual conflictPredationPopulationPredatorDemography

Abstract

fetched live from OpenAlex

Body size often indicates an individual’s quality and so mate selection is typically for larger individuals, including in ladybirds (Coccinellidae). Many organisms including ladybirds are also are known to refuse mating attempts with familiar individuals, but whether at the expense of mating with larger individuals is not clear. We assessed the cumulative effect of body size and familiarity on mating and reproductive behaviour in the zig-zag ladybird beetle (Menochilus sexmaculatus (Fabricius, 1781) = Cheilomenes sexmaculata (Fabricius, 1781)). For this study, individuals were separated into small- and large-bodied individuals and allowed to mate in all possible combinations. Furthermore, following the first mating, an immediate second mating was provided to the males, with either a familiar (same as in the first mating) or an unfamiliar (new female with the same mating status) female. Mating and reproductive parameters were recorded. The mating duration was longer with familiar partners than unfamiliar individuals. Mating duration was shorter for the second mating, suggesting that mating and ejaculate transfer are costly, to assure higher reproductive success. In this ladybird beetle, familiarity modified mating duration, whereas fecundity and egg viability were influenced by body size.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.987

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 head0.009
GPT teacher head0.208
Teacher spread0.198 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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