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Pathway analysis of beneficial bacteria in suppression of plant immunity

2023· article· en· W4390006845 on OpenAlexaff
Sihan Chen

Bibliographic record

VenueTheoretical and Natural Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLegume Nitrogen Fixing Symbiosis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPlant ImmunityImmune systemBiologyImmunityMicroorganismBacteriaImmunologyBiochemistryGeneticsArabidopsis

Abstract

fetched live from OpenAlex

Plants, much like animals, possess immune mechanisms that help protect them against harmful microorganisms and pathogens. Research studies have provided evidence that various detrimental microorganisms can target the immune system of plant roots in the soil, leading to plant diseases. At the same time, recent studies have shown that beneficial microorganisms can also suppress the plant's immune system and form a mutually beneficial relationship with the plant, promoting the colonization of beneficial bacteria and helping the plant to defend itself against other harmful microorganisms. This paper provides a summary of the concept that beneficial bacteria hinder root immunity by inhibiting various downstream reactions within the plant's immune system, based on the understanding of plant root immune mechanisms. The authors also highlight the existing gaps in research on non-invasive microbe-plant interactions and raise questions regarding the intricate interplay between immunity and symbiotic signaling.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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