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Record W2906793203 · doi:10.5539/jas.v11n2p139

Fruit Yield and Antioxidant Activity of Pepper Genotypes Subjected to Nitrogen Doses

2019· article· en· W2906793203 on OpenAlexvenueno aff
Michele de Morais, Paloma E. B. Martins, Milson Evaldo Serafim, Walmes Marques Zeviani, Kelly Lana Araújo, Thiago A. S. Gillio, Renê Arnoux da Silva Campos, Leonarda Grillo Neves

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPiperaceae Chemical and Biological Studies
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de Mato GrossoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsPepperAntioxidantHorticultureGenotypeNitrogenGreenhouseYield (engineering)ChemistryBiologyFood scienceBotanyBiochemistryGene

Abstract

fetched live from OpenAlex

The present study evaluates the fruit yield and antioxidant activity of pepper genotypes as a function of nitrogen doses. The experiment was conducted in a greenhouse with seven pepper genotypes: two C. annuum (116 and 163), two C. chinense (39 and 118), two C. frutescens (17 and 113), and one C. praetermissum (141), and 11 nitrogen doses (0, 1, 2, 4, 8, 16, 32, 64, 128, 256 and 512 N mg dm-3). Productive parameters were evaluated, and antioxidant compounds were determined by spectrophotometric methods. Genotypes 116 and 163 showed a higher fruit fresh mass, and genotype 141 produced the highest number of fruits per plant. Genotypes 141 and 163 were the earliest. The highest antioxidant activity was obtained in the extracts of fruits of genotype 113. Nitrogen fertilization did not affect the antioxidant content of fruits. The pepper genotypes of the present study have comparable bioactive compound contents and antioxidant activity. Therefore, they are promising genotypes for the vegetable production sector, with potential for industrial and pharmacological use.

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 score0.425
Threshold uncertainty score0.190

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 studies0.0000.000
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 head0.070
GPT teacher head0.363
Teacher spread0.293 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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