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Record W3043045539 · doi:10.5539/jas.v12n8p160

Alternative Products to Control Soybean Downy Mildew in the Field

2020· article· en· W3043045539 on OpenAlexvenueno aff
Gislaine Gabardo, Maristella Dalla Pria, Henrique Luis da Silva, Mônica Gabrielle Harms

Bibliographic record

VenueJournal of Agricultural Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogens and Resistance
Canadian institutionsnot available
Fundersnot available
KeywordsDowny mildewFungicideAzoxystrobinAgronomyBiologyMicronutrientHorticultureChemistry

Abstract

fetched live from OpenAlex

Soybean mildew caused by Oomycota Peronospora manshurica, is a disease widely spread in Brazil. In order to study the efficiency of soybean mildew control due to the application of alternative products and fungicide in the field, experiments were conducted in Ponta Grossa, PR, Brazil, during the 2013/2014 and 2014/2015 growing seasons. The design used was randomized blocks with four replications. The treatments were: 1-witness; 2-acibenzolar-S-methyl; 3-calcium; 4-micronutrients: copper, manganese and zinc; 5-micronutrients: manganese, zinc and molybdenum; 6-NK fertilizer; 7-Ascophyllum nodosum and 8-azoxystrobin + cyproconazole with the addition of Nimbus adjuvant. Four applications of alternative products (phenological stages V3, V6, R1 and R5.1) and two of fungicide (phenological stages R1 and R5.1) were performed. The mildew severity was estimated using a diagrammatic scale. The severity data made it possible to calculate the area under the disease progress curve (AUDPC). In the 2014/2015 harvest the disease was more severe. The control of downy mildew by the use of fungicide did not reduce the epidemic. The fungicide was not efficient in the two evaluated seasons. All tested alternative products reduced the disease severity and AUDPC in both seasons. The best results in reducing downy mildew were found with the application of acibenzolar-S-methyl, micronutrients (Cu, Mn, Zn) and A. nodosum.

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)0.001
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 score0.969
Threshold uncertainty score0.175

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.221
Teacher spread0.205 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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