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Record W4365505927 · doi:10.47402/ed.ep.c202321614686

ANÁLISE DA CINÉTICA DE SECAGEM DO FRUTO MELÃO DE SÃO CAETANO (MOMORDICA CHARANTIA L.)

2023· book-chapter· pt· W4365505927 on OpenAlexaff
Luan Gustavo Santos, Alessandra Telis dos Santos, Edmur Gustavo Cabral Scatena, Janaina Dos Reis Bondezan, Karla Fernanda Felette, Tuliana Lorraine Custódio Machado, Raquel Manozzo Galante, Leandro Osmar Werle

Bibliographic record

VenueEditora e-Publicar eBooks · 2023
Typebook-chapter
Languagept
FieldAgricultural and Biological Sciences
TopicBanana Cultivation and Research
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsMomordicaBiologyHumanitiesArtTraditional medicineMedicine

Abstract

fetched live from OpenAlex

O melão São Caetano (Momordica charantia L.) é uma planta alimentícia não convencional rica em compostos bioativos.Seus frutos apresentam capacidade funcionais, capazes de serem eficazes no tratamento de doenças degenerativas.No entanto, seus frutos possuem uma baixa vida-útil devido sua composição rica em água, a qual promove oxidação e degradação.Neste contexto, a secagem quando aplicadas em produtos agroindustriais favorece na preservação do produto, redução do tamanho e redução de atividade biológicas e bioquímicas de produtos agroindustriais.Portanto, este trabalho tem como objetivo avaliar o comportamento cinético da secagem de fatias de melão São Caetano nas temperaturas de 60 e 70 ºC e definir um modelo matemático que represente este processo.O aumento da temperatura de secagem de 60 para 70 ºC promoveu a redução de 30 min do processo, a qual está associada ao aumento da difusividade de 1,581 x 10 -9 para 1,641 x 10 -9 m² s -1 .Além disso, o modelo de Midilli melhor se ajustou aos dados cinéticos, sendo capaz de predizer o processo de desidratação das fatias de melão São Caetano.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.057
GPT teacher head0.275
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractno

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