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Record W2806651123 · doi:10.30612/agrarian.v11i40.6241

Efeito residual da aplicação de silicato de cálcio nos atributos químicos do solo e na produtividade da cana-soca

2018· article· pt· W2806651123 on OpenAlexaff
Alessandra Mayumi Tokura Alovisi, Grazielli Caroline Rocha Aguiar, Alves Alexandre Alovisi, Cezesmundo Ferreira Gomes, Luciene Kazue Tokura, Elaine Reis Pinheiro Lourente, Munir Mauad, Robervaldo Soares da Silva

Bibliographic record

VenueAgrarian · 2018
Typearticle
Languagept
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsWSP (Canada)
Fundersnot available
KeywordsChemistry

Abstract

fetched live from OpenAlex

Trabalhos de pesquisa no Brasil e em outros países, com a utilização de silicato de cálcio, vem mostrando resultados promissores na cultura da cana- de- açúcar. Este trabalho objetivou avaliar o efeito residual da aplicação do silicato de cálcio como material corretivo de acidez do solo, nos atributos químicos do solo e na produtividade da primeira soqueira de cana-de-açúcar. O trabalho foi desenvolvido, em condições de campo, na Fazenda Escola da Anhanguera de Dourados-MS, com a variedade SB803250. O delineamento experimental utilizado foi em blocos casualizados, com quatro repetições. Os tratamentos foram constituídos de doses distintas de silicato de cálcio (0, 700, 1400, 2800, 5600 kg ha-1). No solo, a amostragem foi realizada aos 24 meses após a aplicação do silicato de cálcio, nas camadas de 0-0,2- e 0,20-0,40 m de profundidade, determinando os atributos químicos para fins de fertilidade do solo e a produtividade da cana-soca. O silicato de cálcio promoveu efeito residual benéfico nos atributos de acidez do solo após 24 meses da aplicação. A aplicação do silicato de cálcio, em pré-plantio, promoveu efeito residual positivo na produtividade da soqueira da cana-de-açúcar.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.021
GPT teacher head0.261
Teacher spread0.240 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations6
Published2018
Admission routes1
Has abstractyes

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