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Record W2921246568 · doi:10.5539/jas.v11n4p395

Post-harvest Evaluation of Bananas (Musa sp.) Cultivated in the Brazilian Semi-arid Region When Submitted to Cold Storage

2019· article· en· W2921246568 on OpenAlexvenueno aff
José Aluísio de Araújo Paula, Elizângela Cabral dos Santos, Eudes de Almeida Cardoso, Roberto Pequeno de Sousa, Janilson Pinheiro de Assis, Marta Juvênia Andrade Oliveira Meinerz, Paulo César Ferreira Linhares, Alexandre Lopes Macedo

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBanana Cultivation and Research
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsTitratable acidCultivarHorticultureCold storageChemistryFood scienceBotanyMathematicsBiology

Abstract

fetched live from OpenAlex

This study evaluates the post-harvest quality of fruits of different banana cultivars regarding type of propagation when stored in cold at three different times at constant temperature. The experiment was carried out at the Fazenda Terra Santa in the semi-arid region of northeastern Brazil. We applied a completely randomized experiment in a 6 × 3 factorial, evaluating the post-harvest development of ‘Prata-anã’ and ‘Pacovan’ cultivars, propagated in three different ways and analyzed at three distinct times of storage in the cold. The following fruit quality variables were analyzed: soluble solids, vitamin C content, titratable acidity, and potential of hydrogen (pH). The analysis of variance revealed a significant effect at 1% probability in all sources of variation of analyzed variables of Soluble Solids and pH, for the unfolding treatments × storage time of the analyzed variables Vitamin C and titratable acidity, and for the source of variation treatment of the vitamin C variable. The ‘Prata-anã’ cultivar propagated by rhizome with “ceva” was the most efficient technique of propagation, providing good soluble solids and titratable acidity contents, but with the lowest pH values.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.962
Threshold uncertainty score0.203

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
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 head0.038
GPT teacher head0.281
Teacher spread0.244 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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