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Record W2960795115 · doi:10.3389/fmicb.2019.01682

Metagenomic Functional Shifts to Plant Induced Environmental Changes

2019· article· en· W2960795115 on OpenAlexafffund
Svetlana N. Yurgel, Jacob T. Nearing, Gavin M. Douglas, Morgan G. I. Langille

Bibliographic record

VenueFrontiers in Microbiology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaTerry Fox Research InstituteDalhousie UniversityBeatrice Hunter Cancer Research InstituteCancer Research Institute
KeywordsRhizosphereMicrobiomeMetagenomicsBiologyMicroorganismNicheAdaptation (eye)Soil waterEcologyBacteriaBioinformatics

Abstract

fetched live from OpenAlex

(wild blueberry) agricultural system involves transformation of the environment surrounding the plant to intensify plant propagation and to improve fruit yield, and therefore is an advantageous model to study the interaction between soil microorganisms and plant-host interactions. We studied this system to address the question of a trade-off between microbial adaptation to a plant-influenced environment and its general metabolic capabilities. We found that many basic metabolic functions were similarly represented in bulk soil and rhizosphere microbiomes overall. However, we identified a niche-specific difference in functions potentially beneficial for microbial survival in the rhizosphere but that might also reduce the ability of microbes to withstand stresses in bulk soils. These functions could provide the microbiome with additional capabilities to respond to environmental fluctuations in the rhizosphere triggered by changes in the composition of root exudates. Based on our analysis we hypothesize that the rhizosphere-specific pathways involved in xenobiotics biodegradation could provide the microbiome with functional flexibility to respond to plant stress status.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score1.000

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)0.0010.001

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 head0.015
GPT teacher head0.182
Teacher spread0.168 · 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.

Study designBench or experimental
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

Citations55
Published2019
Admission routes2
Has abstractyes

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