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Record W2792424257 · doi:10.5539/jas.v10n4p306

Technical and Economic Viability of Wheat with Forms of Application and Doses of Boron

2018· article· en· W2792424257 on OpenAlexvenueno aff
Fernando Shintate Galindo, Marcelo Carvalho Minhoto Teixeira Filho, Salatiér Buzetti, Eduardo Henrique Marcandalli Boleta, Willian Lima Rodrigues, José Mateus Kondo Santini, Alexandre R. M. Rosa, Mariana Gaioto Ziolkowski Ludkiewicz, Vinícius Martins Silva

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsnot available
Fundersnot available
KeywordsOxisolStrawSowingAgronomyRandomized block designBoric acidBoronMathematicsHuman fertilizationEnvironmental scienceBiologyChemistrySoil waterSoil science

Abstract

fetched live from OpenAlex

Boron is one of the most limiting micronutrients in grains production system in Brazil. In this way, the objective was to evaluate the effect of forms of application and doses of boron in irrigated wheat grain yield evaluating the economic terms in Cerrado region. The experiment was conducted in no-tillage system in an Oxisol with clay texture in Selvíria, MS, Brazil. The experimental design was a randomized block design with four replicates, arranged in a 4 × 3 factorial scheme: four doses of boron (0, 1, 2 and 4 kg ha-1) with boric acid source (B = 17%); and three apllication forms: a) in desiccation of the predecessor straw, together with herbicide; b) at the time of sowing, in soil along with the formulated fertilization seeding and c) via leaf tissue with the application of post emergent herbicide. The application of 2 kg ha-1 provides greater grain yields, but the highest economic return was obtained at the dose of 1 kg ha-1, with application in soil, ensuring profitability from production of irrigated wheat in the Cerrado.

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 score0.680
Threshold uncertainty score0.196

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 studies0.0000.001
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 head0.006
GPT teacher head0.214
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 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

Citations5
Published2018
Admission routes1
Has abstractyes

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