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Record W2125305044 · doi:10.1039/c2fo30164d

Relationships between molecular structure and kinetic and thermodynamic controls in lipid systems. Part I: propensity for oil loss of saturated triacylglycerols

2012· article· en· W2125305044 on OpenAlexafffund
Laziz Bouzidi, Tolibjon S. Omonov, Nissim Garti, Suresh S. Narine

Bibliographic record

VenueFood & Function · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsUniversity of AlbertaTrent University
FundersTrent University
KeywordsKineticsThermodynamicsChemistryKinetic energyPhase (matter)ThermalOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Pure saturated triacylglycerols (TAGs) in canola oil were used as model systems to analyse oil loss in structured oil both from thermodynamic and kinetic perspectives. Two important parameters which effectively and predictively measure the relative propensity of a solid network to lose/hold oil were defined: (1) the rate of oil loss, K, which is a quantified representation of the kinetics of oil loss and (2) the initial amount of oil susceptible to be lost, i.e., the propensity for oil loss (POL), which is a representation of the thermodynamics of oil binding. It was found that the POL and K values do not always trend in the same fashion, suggesting that the mechanism of oil binding is complex, depending on the structurant's crystalline form locked within the oil network. The two parameters were, however, correlated to the melting and thermal behavior of the structurants, to the polymorphic structures that are obtained during the cooling process and to the habit (shape, size and morphology) of the crystalline phase in the oil. Both POL and K had a strong correlation to the oil loss.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.166

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 head0.030
GPT teacher head0.200
Teacher spread0.171 · 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 designObservational
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

Citations8
Published2012
Admission routes2
Has abstractyes

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