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Record W2010012857 · doi:10.1080/10408398.2011.610906

The Impact of Milling and Thermal Processing on Phenolic Compounds in Cereal Grains

2012· review· en· W2010012857 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

VenueCritical Reviews in Food Science and Nutrition · 2012
Typereview
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
Fundersnot available
KeywordsFood scienceAntioxidantHealth benefitsChemistryDietary fibreExtrusionExtrusion cookingFood processingFood productsDietary fiberWhole grainsMaterials scienceOrganic chemistryTraditional medicineMedicineMetallurgy

Abstract

fetched live from OpenAlex

Consumption of wholegrain foods has been recommended for healthy diets. The beneficial health properties of wholegrain products have been associated with the presence of higher amounts of dietary fiber and antioxidants and lower calories as compared to their respective refined ones. Phenolic compounds are mainly attributed to antioxidant properties of wholegrain foods. This review article provides a single comprehensive source that describes effects of milling and thermal processing on phenolic compounds and antioxidant properties in cereals. In general, milling and pearling processes affect the distribution of phenolic, compounds and thus antioxidant properties vary among the milling fractions. Thermal processes such as baking and extrusion could cause negative or positive effects on phenolic compounds and antioxidant properties of the end product subject to grain type and processing conditions. Thus factors that enhance health benefits of wholegrain cereal products have been discussed.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.154
GPT teacher head0.419
Teacher spread0.264 · 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