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Record W3110650857 · doi:10.5539/jas.v13n1p147

Agronomic Performance of Soybean With Seeds Treated With an Algae Extract Base Biostimulant

2020· article· en· W3110650857 on OpenAlexvenueno aff
Gabriel Bressiani Melo, Alessandro Guerra da Silva, Adriano Perín, Guilherme Braga Pereira Braz, Christiano Lima Lobo de Andrade

Bibliographic record

VenueJournal of Agricultural Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Growth Enhancement Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized block designProductivityBiologyCropAgronomyCrop productivityHorticulture

Abstract

fetched live from OpenAlex

The use of biostimulants in the treatment of soybean seeds can provide beneficial effects on the crop due to the synergism between the organic components present in products from natural compounds. Due to the scarcity of results of research on biostimulants in the treatment of soybean seeds, the objective of this study was to evaluate the effects of doses of a biostimulant in the treatment of seeds, on agronomic characteristics, productivity components and profitability of soybean culture. Therefore, two experiments were carried out in the field in the 2016/17 and 2017/18 harvests, using a randomized block design with six replications and four treatments referring to doses of 0.00; 0.05; 0.10; and 0.15 L 100 kg seeds-1. The agronomic characteristics of soybeans, grain yield and profitability of using the biostimulant were evaluated. From the obtained results, it can be seen that the dose of 0.15 L 100 kg seeds-1 provided greater profitability in both harvests. In addition, doses above 0.12 L 100 kg seed-1 provide higher grain yields and a higher grain mass, in addition to other productivity components.

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 score0.616
Threshold uncertainty score0.198

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.208
Teacher spread0.190 · 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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