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Record W3120464220 · doi:10.1094/pdis-09-20-2089-re

Identification of <i>Pectobacterium versatile</i> Causing Blackleg of Potato in New York State

2021· article· en· W3120464220 on OpenAlexaboutno aff
Xing Ma, Paul Stodghill, Miao Gao, Keith L. Perry, Bryan Swingle

Bibliographic record

VenuePlant Disease · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogenic Bacteria Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBlacklegBiologyPectobacteriumPectobacterium carotovorumPhylogenetic treeBacteriaMicrobiologyGeneticsGeneBotany

Abstract

fetched live from OpenAlex

Soft rot bacteria classified in the Pectobacteriaceae (SRP), including Pectobacterium and Dickeya spp., are responsible for soft rot and blackleg diseases of potato. Since 2014, blackleg outbreaks caused by D. dianthicola have increased in the United States and Canada. Our previous study found that the most abundant causal organisms of blackleg disease in New York State were P. parmentieri and D. dianthicola, with the latter being the only Dickeya species reported. In the present study, we identified and characterized pathogenic SRP bacteria from 19 potato samples collected in New York State during the 2017 growing season. We used genome sequence comparison to determine the pathogens’ species. We found eight P. versatile, one P. atrosepticum, two P. carotovorum, two P. parmentieri, and six D. dianthicola isolates in our 2017 SRP collection. This is the first time that P. versatile has been reported to cause potato blackleg disease in New York State. We determined the phylogenetic relationships between the SRP strains by using 151 single-copy orthologous gene sequences shared among the set of bacteria in our analysis, which provided better resolution than phylogenies constructed with the dnaX gene.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score0.168

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 head0.021
GPT teacher head0.201
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

Citations13
Published2021
Admission routes1
Has abstractyes

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