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Record W2901400304 · doi:10.5539/jas.v10n10p518

Chemical Control of Asian Soybean Rust and Its Effect in the Yield and Quality of Soybean Seeds

2018· article· en· W2901400304 on OpenAlexvenueno aff
Márcia Gabriel, Dionei Schmidt Muraro, Genésio Mário da Rosa, Arci Dirceu Wastowski, Stela Maris Kulczynski, Juliano de Cesar da Silva, Ivan Ricardo Carvalho, Vinícius Jardel Szareski, Vanessa Alba da Silva, Juliano de Oliveira Stumm

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYeasts and Rust Fungi Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFungicidePhakopsora pachyrhiziSoybean rustGerminationRandomized block designYield (engineering)AgronomyBiologyRust (programming language)HorticultureMaterials science

Abstract

fetched live from OpenAlex

The objective of this study was to evaluate the effect of a foliar fungicide commonly used to control Asian soybean rust (Phakopsora pachyrhizi) in the physiological and sanitary quality of soybean seeds. The experiment was carried out during the 2012/13 field season, with six treatments and four replications arranged in a randomized complete block design. The following treatments were evaluated: T1 (no fungicide application); one application at R1 stage; two applications at R1 and R3 stages; three applications at R1, R3, and R5 stages; four applications at R1, R3, R5, and R6 stages and five applications at R1, R3, R5, R6 and R7 stages. Seeds yield, seed weight, viability and vigor assessments were used to measure the impact of foliar fungicide applications on soybean yield and seed quality. The results indicated that four fungicide applications provide higher grain yield, increased seed weight, and germination percentages within commercial standards. In contrast, the number of fungicide applications did not correlate with the sanitary quality of the seeds.

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)0.000
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 score0.571
Threshold uncertainty score0.152

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.261
Teacher spread0.249 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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