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Record W4391135876 · doi:10.1128/msystems.01182-23

Metagenomic analysis of the honey bee queen microbiome reveals low bacterial diversity and Caudoviricetes phages

2024· article· en· W4391135876 on OpenAlexaff
Lílian Caesar, Danny W. Rice, Alison McAfee, Robyn M. Underwood, Carrie Ganote, David R. Tarpy, Leonard J. Foster, Irene L. G. Newton

Bibliographic record

VenuemSystems · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersProject Apis m.L'Oreal USAU.S. Department of AgricultureNational Institute of Food and AgricultureNational Science Foundation
KeywordsMicrobiomeMetagenomicsBiologyDiversity (politics)Honey beeEcologyZoologyBioinformaticsGeneticsSociology

Abstract

fetched live from OpenAlex

The queen caste plays a central role in colony success in eusocial insects, as queens lay eggs and regulate colony behavior and development. Queen failure can cause colonies to collapse, which is one of the major concerns of beekeepers. Thus, understanding the biology behind the queen’s health is a pressing issue. Previous studies have shown that the bee microbiome plays an important role in worker bee health, but little is known about the queen microbiome and its function in vivo . Here, we characterized the queen microbiome, identifying for the first time the present species and their putative functions. We show that the queen microbiome has predicted nutritional and protective roles in queen association and comprises only four consistently present bacterial species. Additionally, we bring to attention the spread of phages in the queen microbiome, which increased in abundance in failing queens and may impact the fate of the colony.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.230
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 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

Citations20
Published2024
Admission routes1
Has abstractyes

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