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Record W1976820328 · doi:10.1007/s11746-003-0838-2

Phase behavior of a binary lipid shortening system: From molecules to rheology

2003· article· en· W1976820328 on OpenAlexaff
K. L. Humphrey, Paul Moquin, Suresh S. Narine

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsAlberta Ministry of Agriculture and ForestryUniversity of Alberta
Fundersnot available
KeywordsMicrostructureMaterials scienceDiffractometerPolymorphism (computer science)RheologyOptical microscopePhase diagramPhase (matter)Analytical Chemistry (journal)CrystallographyComposite materialChromatographyChemistryScanning electron microscopeOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The phase behavior of fully hydrogenated canola oil in soybean oil was investigated using iso‐solid lines from temperature‐controlled pulse‐NMR along with DSC data, with the rate of cooling of crystallized samples kept constant. The molecular diversity within the fat system was investigated using HPLC and GC. The microstructure of the fats was determined using a temperature‐controlled polarized light microscope, and the polymorphism of the solidified fat structures was determined via a temperature‐controlled X‐ray diffractometer. Hardness was measured by a temperature‐controlled Instron mechanical analyzer with a penetration cone. The phase behavior predicted by the DSC and iso‐solid lines did not account for the hardness trends observed, as the microstructure and polymorphism of the fat also played a significant role. The addition of hard fat to a system did not consistently increase the hardness of the fat system. Furthermore, the solution behavior demonstrated by the iso‐solid line diagram did not account for all trends in melting behavior, as both intersolubility and polymorphic changes occurred simultaneously. it was found that variations in hardness can be inferred from structural changes, although the structural level causing variation differs.

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 score0.007
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.011
GPT teacher head0.239
Teacher spread0.227 · 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

Citations38
Published2003
Admission routes1
Has abstractyes

Explore more

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