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Record W2253799983 · doi:10.3168/jds.2015-9917

Short communication: Serum composition of milk subjected to re-equilibration by dialysis at different temperatures, after pH adjustments

2016· article· en· W2253799983 on OpenAlexafffund
Zhengtao Zhao, Milena Corredig

Bibliographic record

VenueJournal of Dairy Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicelleCaseinChemistryDissociation (chemistry)Skimmed milkChromatographyColloidPhosphateCalciumComposition (language)TurbidityWhey proteinFood scienceAqueous solutionBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The objective of this work was to investigate the properties of casein micelles after pH adjustment and their re-equilibration to the original pH and serum composition. Re-equilibration was carried out by dialyzing against skim milk at 2 different temperatures (4 or 22 °C). Turbidity, the average radius of the casein micelles, and the composition of the soluble phase were measured at different pH values, ranging between 5.5 and 8. Acidification led to the solubilization of colloidal calcium phosphate and decrease of the average radius of the micelles. With re-equilibration, casein dissociation occurred. In milk with pH values greater than 6.0, the average radius was recovered after re-equilibration. At pH values greater than neutral, an increase of the radius of casein micelles and increased dissociation of the casein were found. After re-equilibration, the radius of micelles and soluble protein in the serum decreased. The results were not affected by the temperature of re-equilibration. The changes to the calcium phosphate equilibrium and the dissociation of the micelles will have important consequences to the functionality of casein micelles.

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)0.001
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 score0.446
Threshold uncertainty score0.149

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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

Citations20
Published2016
Admission routes2
Has abstractyes

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