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Current Techniques to Study Beneficial Plant-Microbe Interactions

2022· review· en· W4285011486 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicroorganisms · 2022
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBiologyMicrobial inoculantMicrobiomePlant growthMetabolomicsTranscriptomeGenomePlant developmentComputational biologyBacteriaBiotechnologyProteomicsGeneBotanyBioinformaticsGene expressionGenetics

Abstract

fetched live from OpenAlex

Many different experimental approaches have been applied to elaborate and study the beneficial interactions between soil bacteria and plants. Some of these methods focus on changes to the plant and others are directed towards assessing the physiology and biochemistry of the beneficial plant growth-promoting bacteria (PGPB). Here, we provide an overview of some of the current techniques that have been employed to study the interaction of plants with PGPB. These techniques include the study of plant microbiomes; the use of DNA genome sequencing to understand the genes encoded by PGPB; the use of transcriptomics, proteomics, and metabolomics to study PGPB and plant gene expression; genome editing of PGPB; encapsulation of PGPB inoculants prior to their use to treat plants; imaging of plants and PGPB; PGPB nitrogenase assays; and the use of specialized growth chambers for growing and monitoring bacterially treated plants.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.065
GPT teacher head0.316
Teacher spread0.251 · 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