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Record W2087405515 · doi:10.1007/s11746-007-1092-y

Diminishing Marginal Utility of Cooling Rate Increase on the Crystallization Behavior and Physical Properties of a Lipid Sample

2007· article· en· W2087405515 on OpenAlexaff
K. L. Humphrey, Suresh S. Narine

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCrystallizationSample (material)Materials scienceAnalytical Chemistry (journal)ThermodynamicsChemistryChromatographyPhysics

Abstract

fetched live from OpenAlex

Abstract It is well established that variation of the rate of cooling ( r) of a lipid sample is an effective tool to influence the crystallization process and effect changes in network structure and physical functionality. However, the extent of the physical changes does not always justify the extent to which the cooling rate must be altered. It is therefore important to understand the rates at which marginal changes in physical functionality begin to diminish, and to understand the mechanisms which introduce such limitations. A commercially available cocoa butter alternative, Temcote (Bunge Oils, Bradley, IL), was crystallized under cooling rates varying from 0.1 to 20 °C min −1 . The growth mode and polymorphism of each sample was studied using DSC and X‐ray diffraction (XRD). The hardness of the sample was monitored using cone penetrometry and its solid fat content (SFC) evolution was monitored using a temperature controlled pulse‐NMR. The data demonstrates that the melting profile of the sample could be greatly manipulated over a relatively narrow range of cooling rates. Large increases in cooling rate increase the final SFC of the sample by approximately 6%. Doubling the cooling rate increases the hardness of the sample 50%. Variation of the cooling rate as a tool to modify physical functionality of the network was found to be effective only for cooling rates lower than 5 °C min −1 .

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 score0.048
Threshold uncertainty score0.166

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.230
Teacher spread0.209 · 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
Published2007
Admission routes1
Has abstractyes

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