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Record W1989056693 · doi:10.1139/w03-011

Effect of genotype and root colonization in biological control of fusarium wilts in pigeonpea and chickpea by<i>Pseudomonas aeruginosa</i>PNA1

2003· article· en· W1989056693 on OpenAlexvenueno aff
V. Anjaiah, Pierre Cornélis, Nico Koedam

Bibliographic record

VenueCanadian Journal of Microbiology · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsnot available
FundersVlaamse regeringDirection Générale de l’offre de SoinsVrije Universiteit Brussel
KeywordsFusarium wiltBiologyFusarium oxysporumRhizosphereColonizationFusariumWilt diseaseInoculationHorticulturePseudomonasBacteriaMicrobiology

Abstract

fetched live from OpenAlex

Pseudomonas aeruginosa PNA1, an isolate from chickpea rhizosphere in India, protected pigeonpea and chickpea plants from fusarium wilt disease, which is caused by Fusarium oxysporum f.sp. ciceris and Fusarium udum. Inoculation with strain PNA1 significantly reduced the incidence of fusarium wilt in pigeonpea and chickpea on both susceptible and moderately tolerant genotypes. However, strain PNA1 protected the plants from fusarium wilt until maturity only in moderately tolerant genotypes of pigeonpea and chickpea. Root colonization of pigeonpea and chickpea, which was measured using a lacZ-marked strain of PNA1, showed tenfold lower root colonization of susceptible genotypes than that of moderately tolerant genotypes, indicating that this plant-bacteria interaction could be important for disease suppression in this plant. Strain PNA1 produced two phenazine antibiotics, phenazine-1-carboxylic acid and oxychlororaphin, in vitro. Its Tn5 mutants (FM29 and FM13), which were deficient in phenazine production, caused a reduction or loss of wilt disease suppression in vivo. Hence, phenazine production by PNA1 also contributed to the biocontrol of fusarium wilt diseases in pigeonpea and chickpea.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.186
Teacher spread0.180 · 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

Citations134
Published2003
Admission routes1
Has abstractyes

Explore more

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