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A plant growth-promoting pseudomonad is closely related to the<i>Pseudomonas syringae</i>complex of plant pathogens

2011· article· en· W1925725328 on OpenAlexafffund
Andrew J.C. Blakney, Cheryl L. Patten

Bibliographic record

VenueFEMS Microbiology Ecology · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogenic Bacteria Studies
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPseudomonas syringaeBiologyPathovarMicrobiologyVirulencePseudomonadaceae16S ribosomal RNAPseudomonadalesPseudomonas putidaHypersensitive responseBotanyPseudomonasGeneBacteriaPathogenGeneticsPlant disease resistance

Abstract

fetched live from OpenAlex

Pseudomonas putida GR12-2 is well known as a plant growth-promoting rhizobacterium; however, phylogenetic analysis using the 16S rRNA gene and four housekeeping genes indicated that this strain forms a monophyletic group with the Pseudomonas syringae complex, which is composed of several species of plant pathogens. On the basis of these sequence analyses, we suggest that P. putida GR12-2 be redesignated as P. syringae GR12-2. To compare the ecological roles of P. syringae GR12-2 with its close relatives P. syringae pathovar (pv.) tomato DC3000 and P. syringae pv. syringae B728a, we investigated their ability to cause disease and promote plant growth. When introduced on tobacco or tomato leaves, P. syringae GR12-2 was unable to elicit a hypersensitive response or cause disease, which are characteristic responses of P. syringae DC3000 and B728a, nor were type III secretion system genes required for virulence detected in P. syringae GR12-2 by PCR or DNA hybridization. In contrast to P. syringae GR12-2, neither of the phytopathogens was able to promote root growth when inoculated onto canola seeds. Although commensals and nonpathogens have been reported among the strains of the P. syringae complex, P. syringae GR12-2 is a mutualist and a phytostimulator.

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 score0.825
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.199
Teacher spread0.169 · 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

Citations20
Published2011
Admission routes2
Has abstractyes

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