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Record W2751955428 · doi:10.1094/cfw-62-4-0139

Does the Concentration, Isolation, or Deflavoring of Pea, Lentil, and Faba Bean Protein Alter Protein Quality?

2017· article· en· W2751955428 on OpenAlexaff
Matthew G. Nosworthy, Mehmet Tulbek, James D. House

Bibliographic record

VenueCereal Foods World · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Manitoba
Fundersnot available
KeywordsProtein qualityIsolation (microbiology)Pea proteinBiologyChemistryFood scienceAgronomyBioinformatics

Abstract

fetched live from OpenAlex

Protein quality is the characteristic of a protein source that satisfies the requirements for growth and maintenance. Although there is interest in alternatives to animal-based protein, the plant-based alternatives currently available require significant processing prior to human consumption. Certain processes, including protein concentration, isolation, and deflavoring, have direct applications for product development because the processed compounds are high in protein. These processes, however, can alter the quality of the protein produced by changing the amino acid composition or altering the digestibility of the protein. This article provides an overview that describes the effects the techniques of protein concentration, isolation, and deflavoring have on the protein quality of three pulse classes: pea, lentil, and faba bean.

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 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.533

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 studies0.0010.000
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 head0.045
GPT teacher head0.296
Teacher spread0.251 · 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 teacher head, 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

Citations25
Published2017
Admission routes1
Has abstractyes

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