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Record W2954919572 · doi:10.5539/jas.v11n11p185

Components of Maize Crop as a Function of Doses of Polymerized Urea

2019· article· en· W2954919572 on OpenAlexvenueno aff
Luiz Leonardo Ferreira, Uirá do Amaral, Cairo Souza Silva, Carmen Rosa da Silva Curvêlo, Alexandre Igor de Azevedo Pereira

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsUreaHuskAgronomyCultivarNitrogenLeaching (pedology)FertilizerFactorial experimentCropAmmonia volatilization from ureaMathematicsChemistryHorticultureBiologyBotanyBiochemistry

Abstract

fetched live from OpenAlex

The efficiency of nitrogen fertilizer applications such as urea is reduced as a function of volatilization and leaching losses. For this reason, the producers have opted for the use of polymerized urea. Therefore, the objective of this work was to evaluate the production components in maize culture as a function of doses of polymerized urea. The experimental design was a 5 × 3 factorial, totaling fifteen treatments, corresponding to five doses Polyblen® 39% N of polymerized urea (0, 250, 500, 750 and 1000 kg ha-1) and three cultivars of maize (30A37, MG580 and MG600). The variables were analyzed after harvest, being: plant height, ear height commercial, stem diameter, leaf area, plant leaf area, ear diameter without husks, ear diameter with husks, ear length without husks, ear length with husks, number of grains per row, number of grains per ear, grain yield, number of rows per ear, 100-grain weight. Regarding the production components in the corn crop, the presence of Polyblen® polymerized urea influenced all variables analyzed. While varieties 30A37, MG580 and MG600 demonstrated a significant increase in their productivity averages.

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 score0.812
Threshold uncertainty score0.334

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 communication0.0000.000
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 head0.016
GPT teacher head0.220
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

Citations11
Published2019
Admission routes1
Has abstractyes

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