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Record W2040937453 · doi:10.1094/cfw-59-3-0127

Optimizing the Bioactive Potential of Oat Bran by Processing

2014· article· en· W2040937453 on OpenAlexaff
Marta S. Izydorczyk, Stefan Cenkowski, J.E. Dexter

Bibliographic record

VenueCereal Foods World · 2014
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of ManitobaCanadian International Grains Institute
FundersCenters for Disease Control and Prevention
KeywordsBranFood scienceBiotechnologyAvenaChemistryBiochemical engineeringBiologyAgronomyEngineeringRaw material

Abstract

fetched live from OpenAlex

Oats have attracted consumer, research, and commercial interest due to the health benefits associated with their consumption. β-Glucans are major dietary constituents in oats that have been linked to reduced serum cholesterol concentrations in humans, and foods containing oats are allowed to carry a health claim related to the ability of the soluble fiber in oats to reduce the risk of heart disease. However, to receive the recommended amount of β-glucans (3 g/day) from whole oat groats may require consumption of large quantities of oat products. Thus, the production of oat fractions enriched with higher levels of β-glucans is desirable. The most common commercially available oat product with an increased concentration of β-glucans is oat bran. In addition to higher concentrations of β-glucan, other physical and physicochemical properties of oat bran preparations should be considered as part of their production to achieve the optimal and expected health benefits from consumption of such products. Conventio...

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.244
Teacher spread0.231 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

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