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Record W2938474601 · doi:10.5539/jas.v11n5p172

Potassium Silicate, Against Water Stress, in Sweet Corn Plant Growth Traits

2019· article· en· W2938474601 on OpenAlexvenueno aff
Ausbie Luis Graça Araújo, Amanda Maria de Almeida, João de Jesus Guimarães, Fernando Soares de Cantuário, Leandro Caixeta Salomão, Carmen Rosa da Silva Curvêlo, Aurélio Rúbio Cantuário, José Magno Queiróz Luz, Alexandre Igor de Azevedo Pereira

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de GoiásConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsPotassium silicateSeedingPotassiumAgronomyRandomized block designFactorial experimentSoil waterHorticultureChemistryBiologySilicateMathematics

Abstract

fetched live from OpenAlex

Water stress in sweet corn plants due effect of climatic events, such as El Niño, is difficult to monitor, leading to considerable losses. Silicon (Si) as an exogenous resistance elicitor may reduce water stress effects. The relationship between sweet corn plant age and its development, under induced water stress and leaf potassium silicate applications were evaluated. This work was carried out with the hybrid Tropical Plus®, in a randomized factorial block design with 15, 30, 45 and 60 kPa as soil water tensions in plots and potassium silicate doses (0, 6, 12 and 24 L ha-1) in subplots. Stem diameter, plant height and leaf number per plant were evaluated at 30, 45, 60, 75 and 90 days after seeding. Root length was measured on the 90th day after seeding. Sweet corn plants submitted to water stress conditions and Si application showed an age-dependent response. Water stress did not decrease stem diameter, plant height and number of leaves per plant sprayed with Si. Root length was longer with 60 kPa soil water tension. Silicon reduced negative impacts of water stress on sweet corn plants.

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)0.000
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 score0.806
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.196
Teacher spread0.188 · 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
Published2019
Admission routes1
Has abstractyes

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