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Record W2964179349 · doi:10.5539/jas.v11n13p226

Bacillus thuringiensis Combined With Fungicide Applications in the Management of Soybean Leaf Diseases

2019· article· en· W2964179349 on OpenAlexvenueno aff
Mônica Anghinoni Müller, Ana Cláudia Klosowski, Maristella Dalla Pria, Sérgio Miguel Mazaro, Louise Larissa May De Mio

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYeasts and Rust Fungi Studies
Canadian institutionsnot available
FundersFundação AraucáriaConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsSoybean rustPhakopsora pachyrhiziFungicidePowdery mildewBiologyDowny mildewAgronomyBacillus thuringiensisHorticultureBacteria

Abstract

fetched live from OpenAlex

Asian soybean rust (ASR) caused by Phakopsora pachyrhizi is the main soybean leaf disease in Brazil. Downy mildew (Peronospora manshurica) and powdery mildew (Microsphaera diffusa) are important soybean foliar diseases of occurrence in Paraná state. To reduce severity and yield losses caused by soybean foliar diseases, this work aimed to verify the efficacy of different application doses of the commercial product Dipel® Bacillus thuringiensis (Bt), which is a biological insect controller, combined with number of Opera® fungicide (pyraclostrobin + epoxiconazole) intercalated applications, and to investigate the effect of the Bt Dipel® in the resistance induction of soybean. For this study, different methodologies were used, including in vitro, in vivo and field assays. The results showed that Bt Dipel® can reduce soybean foliar diseases by inducing β-1,3 glucanase enzime and phytoalexin gliceolin. The fungicide application in the experiment condition reduced yield losses.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.093

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 head0.005
GPT teacher head0.213
Teacher spread0.208 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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