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Record W2789330017 · doi:10.1080/10408398.2018.1436038

Exploration of collagen recovered from animal by-products as a precursor of bioactive peptides: Successes and challenges

2018· review· en· W2789330017 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 · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of Manitoba
FundersMinisteriet for Fø devarer, Landbrug og Fiskeri
KeywordsGelatinFunctional foodBioavailabilityChemistryBiotechnologyFood industryBiochemistryPharmacologyFood scienceBiology

Abstract

fetched live from OpenAlex

A large amount of food-grade animal by-products is annually produced during industrial processing and they are normally utilized as animal feed or other low-value purposes. These by-products are good sources of valuable proteins, including collagen or gelatin. The revalorization of collagen may lead to development of a high benefit-to-cost ratio. In this review, the major approaches for generation of collagen peptides with a wide variety of bioactivities were summarized, including antihypertensive, antioxidant and antidiabetic activities, and beneficial effects on bone, joint and skin health. The biological potentials of collagen peptides and their bioavailability were reviewed. Moreover, the unique advantages of collagen peptides over other therapeutic peptides were highlighted. In addition, the current challenges for development of collagen peptides as functional food ingredients were also discussed. This article discusses the opportunity to utilize collagen peptides as high value-added bio-functional ingredients in the food industry.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.811
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.114
GPT teacher head0.368
Teacher spread0.254 · 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