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Record W2939958195 · doi:10.5539/jas.v11n5p206

News Soybean Sowing Arrangements on Spray and Control of Asian Rust

2019· article· en· W2939958195 on OpenAlexvenueno aff
Diego Miranda de Souza, Leidiane Coelho Carvalho, Marina Mouzinho Carvalho, R. C. O. de F. Bueno, Carlos Gilberto Raetano

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYeasts and Rust Fungi Studies
Canadian institutionsnot available
FundersUniversidade Estadual Paulista
KeywordsSowingPhakopsora pachyrhiziSoybean rustAgronomyFungicideYield (engineering)Phytosanitary certificationRust (programming language)CropBiologySeed treatmentMathematicsHorticultureGerminationComputer science

Abstract

fetched live from OpenAlex

Asian soybean rust (ASR) is one of the most severe diseases of the soybean crop, and the use of fungicides is the main form of control. Among the updates of soybean production system is adoption of new sowing arrangements. However are still incipient the studies to combine soybean sowing arrangements and pesticide spraying techniques on phytosanitary treatments on this crop. Therefore, the study aimed to evaluate the impact of different soybean sowing arrangements on spraying, ASR control, agronomic parameters and yield. The experimental design was randomized blocks in the subdivided plots scheme, with five sowing arrangements and two application techniques (with and without adjuvant). The spray deposition and coverage levels, the vertical distribution of leaves in the plants on different sowing arrangements, were evaluated the ASR disease severity and the productivity. Spraying is not affected by sowing arrangements. The adjuvant increased spray coverage. The vertical distribution of the leaves and number of branches in the soybean plants is affected by the sowing arrangement. The disease severity and yield were not affected by sowing arrangements.

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 score0.823
Threshold uncertainty score0.138

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 head0.221
Teacher spread0.216 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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