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Record W4220996866 · doi:10.5772/intechopen.102603

Beyond Bread and Beer: Value-Added Products from Wheat

2022· book-chapter· en· W4220996866 on OpenAlex
Timothy J. Tse, Farley Chicilo, Daniel Wiens, Martin J. T. Reaney

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIntechOpen eBooks · 2022
Typebook-chapter
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Saskatchewan
FundersMitacs
KeywordsSecaleFermentationStillageValue addedStrawAgronomyFood scienceRenewable resourceBiofuelBiotechnologyBiologyRenewable energy

Abstract

fetched live from OpenAlex

Although wheat (Triticum aestivum) and related cereals [Barley (Hordeum vulgare), Rye (Secale cereale) are primarily used for producing baked goods and beverages, cereal crops can be used to create many value-added goods beyond these traditional products. Fractionation of cereal grains and extraction of valuable phytochemicals allows greater access to materials for use in food additives and nutritional supplements. Fermentation for beverage and fuel bioethanol production results in not only renewable fuel, but also a range of other coproducts, including nootropics. In addition to traditional grain fermentation, straw fermentation is also discussed, which further utilizes the whole plant. The main by-product of cereal grain fermentation, wheat stillage, can undergo a range of processes to enhance its value as a animal feeds, as well as extraction of useful compounds. These methods provide a glimpse of the many sequential and divergent processes that may bring us closer to realizing the full potential of wheat and related cereal grains.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.866
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.233
Teacher spread0.209 · 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