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Record W2069991902 · doi:10.1021/jf901271v

Physicochemical Properties of β-Glucan in Differently Processed Oat Foods Influence Glycemic Response

2009· article· en· W2069991902 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2009
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsGlycemic Index LaboratoriesAgriculture and Agri-Food Canada
Fundersnot available
KeywordsPostprandialGlucanFood scienceGlycemicChemistryGlycemic indexDepolymerizationBeta-glucanDigestion (alchemy)Functional foodCarbohydrateBiochemistryDiabetes mellitusBiologyEndocrinologyChromatography

Abstract

fetched live from OpenAlex

To assess the effect of food processing on the capacity of oat beta-glucan to attenuate postprandial glycemia, isocaloric crisp bread, granola, porridge, and pasta containing 4 g of beta-glucan as well as control products with low beta-glucan content were prepared. The physicochemical properties (viscosity, peak molecular weight (M(p)), and concentration (C)) of beta-glucan in in-vitro-digestion extracts were evaluated, and fasting and postprandial blood glucose concentrations were measured in human subjects. Porridge and granola had the highest efficacy in attenuating the peak blood glucose response (PBGR) because of their high M(p) and viscosity. beta-Glucan depolymerization in bread and pasta reduced beta-glucan bioactivity. Pastas, known to have low glycemic responses, showed the lowest PBGR. The analyses of these products with previously reported data indicated that 73% of the bioactivity in reducing PBGR can be explained by M(p) x C. Characterizing the physicochemical properties of beta-glucan in bioactive foods aids functional food development.

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.

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 score0.050
Threshold uncertainty score0.365

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 head0.011
GPT teacher head0.213
Teacher spread0.201 · 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