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Record W4381996675 · doi:10.3389/fmicb.2023.1198131

Pseudomonas spp. can help plants face climate change

2023· review· en· W4381996675 on OpenAlexafffund
Antoine Zboralski, Martin Filion

Bibliographic record

VenueFrontiers in Microbiology · 2023
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsPseudomonasAgricultureClimate changeApoplastRhizobacteriaBiologySalinityBacteriaBiotechnologyEnvironmental scienceAgronomyRhizosphereEcologyBotanyCell wall

Abstract

fetched live from OpenAlex

Climate change is increasingly affecting agriculture through droughts, high salinity in soils, heatwaves, and floodings, which put intense pressure on crops. This results in yield losses, leading to food insecurity in the most affected regions. Multiple plant-beneficial bacteria belonging to the genus Pseudomonas have been shown to improve plant tolerance to these stresses. Various mechanisms are involved, including alteration of the plant ethylene levels, direct phytohormone production, emission of volatile organic compounds, reinforcement of the root apoplast barriers, and exopolysaccharide biosynthesis. In this review, we summarize the effects of climate change-induced stresses on plants and detail the mechanisms used by plant-beneficial Pseudomonas strains to alleviate them. Recommendations are made to promote targeted research on the stress-alleviating potential of these bacteria.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.952
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.282
Teacher spread0.217 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations43
Published2023
Admission routes2
Has abstractyes

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