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Record W2891407845 · doi:10.3390/molecules23102444

Bioprocessing of Functional Ingredients from Flaxseed

2018· review· en· W2891407845 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

VenueMolecules · 2018
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsUniversity of Ottawa
FundersUniversity of Otago
KeywordsNutraceuticalBioprocessFunctional foodBioavailabilityHealth benefitsFood scienceChemistryBiotechnologyTraditional medicineBiologyPharmacologyMedicine

Abstract

fetched live from OpenAlex

L.) are oilseeds endowed with nutritional constituents such as lignans, lipids, proteins, fibre, carbohydrates, and micronutrients. Owing to their established high nutritional profile, flaxseeds have gained an established reputation as a dietary source of high value functional ingredients. Through the application of varied bioprocessing techniques, these essential constituents in flaxseeds can be made bioavailable for different applications such as nutraceuticals, cosmetics, and food industry. However, despite their food and health applications, flaxseeds contain high levels of phytotoxic compounds such as linatine, phytic acids, protease inhibitors, and cyanogenic glycosides. Epidemiological studies have shown that the consumption of these compounds can lead to poor bioavailability of essential nutrients and/or health complications. As such, these components must be removed or inactivated to physiologically undetectable limits to render flaxseeds safe for consumption. Herein, critical description of the types, characteristics, and bioprocessing of functional ingredients in flaxseed is presented.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.283

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 head0.079
GPT teacher head0.282
Teacher spread0.203 · 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