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Record W3022042754 · doi:10.1590/0103-8478cr20190653

Nutritional value of gluten-free rice and bean based cake mix

2020· article· en· W3022042754 on OpenAlexaff
P. Z. Bassinello, Juliana Aparecida Correia Bento, Luciana de Oliveira Froes Gomes, Márcio Caliari, B. Dave Oomah

Bibliographic record

VenueCiência Rural · 2020
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsAgriculture and Agri-Food Canada
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsFood scienceGluten freeGlutenStarchWheat flourDietary fiberChemistryMathematics

Abstract

fetched live from OpenAlex

ABSTRACT: Gluten-free foods are a growing trend in the domestic and international markets, contributing to health and wellness, especially the celiac public. The objective of this study was to create a new option of gluten-free food by replacing wheat flour by rice grit flour, cornstarch, and extruded split bean flour, and to aggregate value to low cost industrial byproducts. A new food product was developed and its components analyzed for their nutritional characteristics and changes during baking. The baked cakes can be a source of protein, had a good starch and protein digestibility, and low antinutritional factors, notably tannins. Incorporation of an extruded bean flour in a gluten-free cake mix improves protein digestibility, total dietary fiber, and raises ash, iron, and zinc contents.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.355

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 head0.018
GPT teacher head0.227
Teacher spread0.208 · 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

Citations27
Published2020
Admission routes1
Has abstractyes

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