MétaCan
Menu
Back to cohort
Record W2526785120 · doi:10.1098/rsbl.2016.0429

Mimics here and there, but not everywhere: Müllerian mimicry in<i>Ceroglossus</i>ground beetles?

2016· article· en· W2526785120 on OpenAlexafffund
Carlos P. Muñoz-Ramírez, Pierre‐Paul Bitton, Stéphanie M. Doucet, L. Lacey Knowles

Bibliographic record

VenueBiology Letters · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Windsor
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsMimicryBiologySympatric speciationInterspecific competitionMüllerian mimicrySympatryIntraspecific competitionEvolutionary biologySimilarity (geometry)ZoologyConvergent evolutionEcologyPhylogenetic treeGenetics

Abstract

fetched live from OpenAlex

The ground beetle genus Ceroglossus contains co-distributed species that show pronounced intraspecific diversity in the form of geographical colour morphs. While colour morphs among different species appear to match in some geographical regions, in others, there is little apparent colour matching. Mimicry is a potential explanation for covariation in colour patterns, but it is not clear whether the degree of sympatric colour matching is higher than expected by chance given the obvious mismatches among morphs in some regions. Here, we used reflectance spectrometry to quantify elytral coloration from the perspective of an avian predator to test whether colour similarity between species is, indeed, higher in sympatry. After finding no significant phylogenetic signal in the colour data, analyses showed strong statistical support for sympatric colour similarity between species despite the apparent lack of colour matching in some areas. We hypothesize Müllerian mimicry as the responsible mechanism for sympatric colour similarity in Ceroglossus and discuss potential explanations and future directions to elucidate why mimicry has not developed similar levels of interspecific colour resemblance across space.

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 score0.854
Threshold uncertainty score0.205

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 head0.032
GPT teacher head0.201
Teacher spread0.169 · 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

Citations22
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueBiology LettersSame topicPlant and animal studiesFrench-language works237,207