MétaCan
Menu
Back to cohort
Record W6931360076 · doi:10.5281/zenodo.5095115

Phylogeography of cavity-nesting honeybees (Apis)

2023· other· en· W6931360076 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPhylogenetic treePhylogeographyLineage (genetic)MainlandCladePopulationPhylogeneticsEndemism

Abstract

fetched live from OpenAlex

We examine phylogenetic relationships among species and populations of Asian cavity-nesting honeybees, emphasizing detection of potential unrecognized species in the geographically widespread <em>Apis cerana</em> Fabricius (Hymenoptera, Apidae). We carried out a phylogenetic analysis of genome-wide single nucleotide polymorphisms (SNPs) using BEASTv1.8.4 and IQ-TREE 2. Our samples cover the largest geographic area and number of populations of Asian cavity-nesting honeybees sampled to date. Nodes in the tree were calibrated using the mid-Miocene giant honeybee <em>Apis lithohermaea</em> Engel. We used STRUCTURE, Bayes Factor Delimitation, and discriminant analysis of principal components to infer probable species among populations of cavity-nesting honeybees currently recognized as <em>Apis cerana</em>. Our results support four species within <em>A. cerana</em>: the yellow "plains" honeybee of India and Sri Lanka; the lineage inhabiting the oceanic Philippine islands; the Sundaland lineage found in Indonesia, Malaysia and parts of southeast Asia; and a Mainland lineage, which we provisionally consider <em>A. cerana</em> in a narrow sense.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.354
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.016
GPT teacher head0.245
Teacher spread0.229 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicMachine Learning in BioinformaticsFrench-language works237,207