MétaCan
Menu
Back to cohort

Effect of High-Hydrostatic Pressure and Temperature on Rheological Characteristics of Glycomacropeptide

2003· article· en· W2072435514 on OpenAlexafffund
Hosahalli S. Ramaswamy

Bibliographic record

VenueJournal of Dairy Science · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsMcGill University
FundersMcGill University
KeywordsRheologyShear rateRheometerShear thinningApparent viscosityArrhenius equationActivation energyThermodynamicsHydrostatic pressureChemistryShear stressMaterials scienceViscosityComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

The influences of high pressure and temperature on the rheological characteristics of glycomacropeptide (GMP) were studied using a controlled rate rheometer. GMP dispersions at a concentration of 12.5% (w/w) were subjected to high pressure from 100 to 400 MPa for 30 min and temperature from 20 to 80 degrees C for 15 min followed by rheological measurements at a shear rate ranged between 0 and 200 s-1. Shear stress-shear rate data of both pressure and heat induced GMP samples fitted Herschel-Bulkley model well with yield stress. It exhibited shear-thinning behavior with flow behavior index ranged between 0.882 and 0.996. Consistency coefficient and apparent viscosity increased with pressure up to 300 MPa while those parameters decreased at 400 MPa. The rheology of GMP was influenced by temperature. The consistency coefficient and apparent viscosity at 100 s-1 obeyed the Arrhenius relationship with activation energies ranged between 8.17 to 12.38 kJ/mol. Lower activation energy signified lesser molecular aggregation or unfolding of protein molecules during thermal treatment of GMP.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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

Citations33
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Dairy ScienceSame topicProteins in Food SystemsFrench-language works237,207