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Record W2216878349 · doi:10.1590/0100-2945-052/14

ADUBAÇÃO NITROGENADA E POTÁSSICA EM GOIABEIRAS ‘PALUMA’:II. EFEITO NO ESTADO NUTRICIONAL DAS PLANTAS

2015· article· pt· W2216878349 on OpenAlexaff
Daniel Angelucci de Amorim, Henrique Antunes de Souza, Danilo Eduardo Rozane, Rafael Marangoni Montes, William Natale

Bibliographic record

VenueRevista Brasileira de Fruticultura · 2015
Typearticle
Languagept
FieldAgricultural and Biological Sciences
TopicGrowth and nutrition in plants
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHorticultureChemistryRandomized block designBiology

Abstract

fetched live from OpenAlex

RESUMOA adequada adubação mineral de pomares de goiabeira, sob manejo intensivo, é fator preponderante na produtividade, e o acompanhamento do estado nutricional das plantas contribui para a eficiência desta prática agronômica.Objetivou-se pesquisar o efeito de diferentes doses de nitrogênio e potássio sobre o estado nutricional de goiabeiras‘Paluma’. O experimento foi conduzido em Vista Alegre do Alto-SP, em pomar irrigado, com sete anos deidade, manejado com podas de frutificação, durante quatro ciclos de produção consecutivos. O solo é o Argissolo Vermelho-Amarelo distrófico. O delineamento experimental foi em blocos ao acaso, com trêsrepetições, em esquema fatorial com quatro doses de nitrogênio (0; 0,5; 1,0 e 2,0 kg planta-1 de N) e quatro depotássio (0;0,55; 1,1 e 2,2 kg planta-1 de K2O). A adubação nitrogenada promoveu aumento nos teores foliares de N e Mn e decréscimo nos teores de P e B, observados do segundo ao quarto ciclo produtivo. A adubaçãonitrogenada elevou os teores de Ca e Mg, respectivamente, no segundo e terceiro ciclos. Com exceção doprimeiro ciclo produtivo, os teores foliares de K e Mn aumentaram em função da adubação potássica, enquanto os teores de Mg, no segundo e quarto ciclos, diminuíram em função dessa adubação.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.036
GPT teacher head0.265
Teacher spread0.229 · 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 designNot applicable
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

Citations8
Published2015
Admission routes1
Has abstractyes

Explore more

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