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Record W4394197185 · doi:10.6084/m9.figshare.21057949

Identification and genome-wide analysis of <i>Bacillus strains</i> JK19 and JK23, two potential biocontrol agents against plant pathogens

2022· dataset· en· W4394197185 on OpenAlexaff
Lijuan Zhang, Wei Huang, Ning Wang, Bo Song, Yi Luo, Jing Zhu, Wei Wang

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsBiologyBiological pest controlIdentification (biology)MicrobiologyGenomeBacillus (shape)Computational biologyGeneticsBotanyGene

Abstract

fetched live from OpenAlex

Bacillus strains JK19 and JK23 have shown enhanced resistance against various plant pathogens, but the whole genomes and molecular mechanisms of these potential biocontrol agents remain poorly understood. In this study, genome-wide sequencing of these strains was performed with the Illumina HiSeq 4000 system. The JK19 genome (4,222,899 bp) was slightly smaller than the JK23 genome (3,996,105 bp), and 4041 and 3823 protein-coding genes were predicted for JK19 and JK23, respectively. The total number of single nucleotide polymorphisms (SNPs) in the coding sequence (CDS) region was 44,023 in JK19 and 44,030 in JK23, while the numbers of insertions and deletions (indels) detected were 343 and 361, respectively, for JK19 and 370 and 366, respectively, for JK23. The phylogenetic trees indicated that JK19 and JK23 are closely related to Bacillus velezensis YAU B9601-Y2, and next-generation sequencing confirmed that both strains belong to B. velezensis. This characterisation of the whole-genome sequences of B. velezensis JK19 and JK23 has broad potential applications in the field of plant disease resistance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.230
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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