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Record W3043757485 · doi:10.1093/femsec/fiaa141

Gut microbiome of the emerald ash borer, Agrilus planipennis Fairmaire, and its relationship with insect population density

2020· article· en· W3043757485 on OpenAlexafffund
Judith Mogouong, Philippe Constant, Robert Lavallée, Claude Guertin

Bibliographic record

VenueFEMS Microbiology Ecology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect symbiosis and bacterial influences
Canadian institutionsCanadian Forest ServiceNatural Resources CanadaInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEmerald ash borerAgrilusBuprestidaeBiologyEcologyPopulation densityInsectPopulationFraxinusZoologyDemography

Abstract

fetched live from OpenAlex

The gut microbial communities of beetles play crucial roles in their adaptive capacities. Environmental factors such as temperature or nutrition naturally affect the insect microbiome, but a shift in local conditions like the population density on a host tree could also lead to changes in the microbiota. The emerald ash borer (EAB), Agrilus planipennis Fairmaire, is an exotic wood borer that causes environmental and economic damage to ash trees in North America. This study aimed to describe the taxonomic structure of the EAB gut microbiome and explore its potential relationship with borer population size. The number of EAB adults collected per tree through a 75 km transect from an epicenter allowed the creation of distinct classes of population density. The Gammaproteobacteria and Ascomycota predominated in bacterial and fungal communities respectively, as determined by sequencing of the bacterial 16S rRNA gene and the fungal internal transcribed spacer ITS2. Species richness and diversity of the bacterial community showed significant dependence on population density. Moreover, α-diversity and β-diversity analysis revealed some indicator amplicon sequence variants suggesting that the plasticity of the gut microbiome could be related to the EAB population density in host trees.

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 score0.709
Threshold uncertainty score0.245

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 head0.022
GPT teacher head0.185
Teacher spread0.164 · 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

Citations20
Published2020
Admission routes2
Has abstractyes

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