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Record W2048885431 · doi:10.1021/jf047959g

Precipitation of Cheddar Cheese Whey Lipids by Electrochemical Acidification

2005· article· en· W2048885431 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

VenueJournal of Agricultural and Food Chemistry · 2005
Typearticle
Languageen
FieldEngineering
TopicMembrane-based Ion Separation Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsChemistryElectrodialysisPrecipitationDemineralizationWhey proteinChromatographyFood scienceGlobules of fatComposition (language)CentrifugationSalt (chemistry)Fraction (chemistry)MembraneBiochemistryMilk fat

Abstract

fetched live from OpenAlex

Lipid separation from cheddar cheese whey allows a better valorization of protein fractions. In this study, bipolar membrane electroacidification (BMEA) was used to obtain precipitates with a high level of lipids. Whey samples with normal and low (by way of electrodialysis) mineral salt levels have been treated by a BMEA process and centrifuged. The composition of flocs and precipitation yields were determined. The BMEA process increased lipid precipitation rates by almost 50% in comparison with a centrifugation step only whereas a demineralization step prior to electroacidification had a limited effect on the precipitation level. Precipitates obtained were mainly composed of lipids (probably phospholipids) but also contained proteins. BMEA of cheddar cheese whey would allow the production of a lipid-enriched fraction and of a protein-enriched whey.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.297

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 head0.005
GPT teacher head0.199
Teacher spread0.194 · 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