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Record W2045282736 · doi:10.1007/s11746-004-0884-9

Modeling the nucleation and crystallization kinetics of a palm stearin/canola oil blend and lard in bulk and emulsified form

2004· article· en· W2045282736 on OpenAlexafffund
Shawn Campbell, H. Douglas Goff, Dérick Rousseau

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsToronto Metropolitan UniversityUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNucleationPalm stearinCrystallizationCanolaHomogenization (climate)Volume fractionMaterials scienceThermodynamicsKineticsChemistryChemical engineeringChromatographyPalm oilOrganic chemistryComposite materialFood science

Abstract

fetched live from OpenAlex

Abstract Using high‐pressure homogenization to generate different droplet size distributions, the nucleation and crystallization of two fat systems [lard or a plam stearin/canola oil blend (PSCO)] were compared in bulk and emulsified form. Droplet size reduction decreased the final volume fraction of solid fat primarily in the lard system vs. the PSCO system, with a greater reduction in volume fraction using the homogenization regime that led to smaller droplets. Homogeneous and heterogeneous models showed that the nucleation rate generally decreased with a reduction in droplet size. However, the Gibbs surface energy (γ) was significantly underestimated using the homogeneous model, whereas the heterogeneous model fit the data adequately ( P <0.05). The temperature sensitivity of the calculated impurity concentration in all emulsified systems was droplet size dependent. The Avrami model showed the emulsified fats to have lower Avrami indices relative to the bulk fat as well as lower crystallization rate constants. Differences in the Avrami indices and the rate constants were more pronounced in the bulk and emulsified PSCO compared with lard.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.110

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 head0.010
GPT teacher head0.204
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

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

Citations18
Published2004
Admission routes2
Has abstractyes

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