MétaCan
Menu
Back to cohort

Quantitative relationships of Pseudomonas chlororaphis 63-28 to Pythium root rot and growth in hydroponic peppers

2011· article· en· W2063439473 on OpenAlexafffund
Coralie R. Sopher, J.C. Sutton

Bibliographic record

VenueTropical Plant Pathology · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPseudomonas chlororaphisRoot rotPythium aphanidermatumBiologyPythiumInoculationHorticultureBrowningPseudomonasBotanyBiological pest controlBacteria

Abstract

fetched live from OpenAlex

The ability of Pseudomonas chlororaphis 63-28 to suppress Pythium root rot (Pythium aphanidermatum) and promote plant growth was investigated in hydroponic peppers that were predisposed or not predisposed to the disease. The biocontrol agent was introduced into the nutrient solution 10 days before the roots were inoculated with the pathogen. The root zone was maintained at 23ºC except when roots were exposed to 33ºC for three days before inoculation to induce predisposition to root rot. At constant 23ºC (no predisposition) application of P. chlororaphis at 10(7) CFU mL-1 nutrient solution delayed root browning more effectively than did higher or lower densities. In predisposed plants, densities of 10(6), 10(7) and 10(8) CFU mL-1 were equally superior. When applied at 10(7) CFU mL-1, the density of P. chlororaphis on roots of the two temperature regimes ranged from log10 5.88 to 6.45 CFU g-1 fresh roots at seven to 19 days after application. The agent delayed root browning, re-mediated predisposition to root rot, and increased growth of inoculated and non-inoculated plants. Leaf expansion was a sensitive marker of root rot and remediation by P. chlororaphis. We conclude that P. chlororaphis 63-28 has substantial potential for managing the disease regardless of predisposition.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.054
GPT teacher head0.240
Teacher spread0.185 · 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 designObservational
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

Citations13
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueTropical Plant PathologySame topicPlant-Microbe Interactions and ImmunityFrench-language works237,207