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Record W2922034692 · doi:10.5539/jas.v11n4p470

Quality of the Pulp of Passion Fruit Produced in the Brazilian Savanna

2019· article· en· W2922034692 on OpenAlexvenueno aff
Francielly Rodrigues Gomes, Pedro Henrique Magalhães de Souza, Marcelo Marchi Costa, Darly G. de Sena-Júnior, Ana Laura Pereira Souza, Victória M. Azevedo, Luciana Celeste Carneiro, Simério Carlos Silva Cruz, Cláudio Hideo Martins da Costa, Danielle F. P. da Silva

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGrowth and nutrition in plants
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsPassion fruitCultivarPulp (tooth)OrchardHorticultureTitratable acidMathematicsBotanyBiologyAgronomy

Abstract

fetched live from OpenAlex

Because it is a species of tropical climate, the passion fruit is distributed widely in South America, mainly in Brazil. Its cultivation represents approximately 95% of the commercial orchards of the country, nevertheless, it presents productivity below its productive potential, being necessary to obtain cultivars adapted to the climate of the regions of culture. The objective of this work was to evaluate the quality of the fruit pulp of yellow passion fruit produced in Jataí-GO as well as the correlations between some physical and chemical characteristics. The fruits were collected in an experimental orchard and sectioned transversely to obtain the pulp. The characteristics of acidity, vitamin C content, soluble solids content, soluble solids/acidity ratio (Ratio), and pulp color parameters were evaluated through the coordinates L*, a*, b*, C* and h* of yellow passion fruit and of the cultivar FB 200. Data were analyzed by means of analysis of variance and Pearson’s correlation at 5% of significance with the aid of the statistical program Rbio. It is concluded that the characteristics of the fruits of the cultivar FB200 differed from the fruits of yellow passion fruit and that these characteristics correlated significantly and positively.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.194

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 communication0.0000.000
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 head0.024
GPT teacher head0.256
Teacher spread0.232 · 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 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

Citations4
Published2019
Admission routes1
Has abstractyes

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