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Record W3094688650 · doi:10.1590/fst.23020

Consumer profile: blackberry processing with different types of sugars

2020· article· en· W3094688650 on OpenAlexaff
Natália Ferreira Suárez, Rafael Azevedo Arruda de Abreu, Letícia Alves Carvalho Reis, Paula Nogueira Curi, Maria Cecília Evangelista Vasconcelos Schiassi, Vanessa Rios de Souza, Rafael Pio

Bibliographic record

VenueFood Science and Technology · 2020
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsUniversity of Guelph
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsSugarFood scienceArtificial SweetenerChemistryBiotechnologyBiology

Abstract

fetched live from OpenAlex

Due to its high medicinal and nutritional values, blackberries have become increasingly interesting to producers and consumers. People are looking for healthier options to consume sugar with greater nutritional enrichment. However, replacement of the type of sugar is associated with significant changes in some parameters, finding suitable replacements that result in satisfactory products can be challenging. The aim of this study was to evaluate the influence of different sugars (white refined sugar, white crystal sugar, demerara sugar, brown sugar and coconut sugar) on the physicochemical, physical and sensory aspects of blackberry juices and jellies. The type of sugar influenced the physicochemical and physical characteristics of blackberry jelly and juice, which reflected the differences in acceptability of the final product. Information on the type of sugar and its benefits influenced the sensory acceptance of blackberry jelly and juice.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0230.002

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 head0.016
GPT teacher head0.245
Teacher spread0.228 · 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 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

Citations5
Published2020
Admission routes1
Has abstractyes

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