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Record W2007944147 · doi:10.1007/s11746-012-2049-3

Organogels: An Alternative Edible Oil‐Structuring Method

2012· article· en· W2007944147 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsWaxPolymerStructuringOrganic chemistryChemistryEdible oilChemical engineeringMaterials sciencePolymer scienceFood science

Abstract

fetched live from OpenAlex

Abstract Structuring liquid oils has become an active area of research in the past decade, mainly due to pressures to reduce saturated fat intake and eliminate trans fats from our diets. However, replacing hard fats with liquid oil can lead to major changes in the quality of food products. Recent strategies to impart solid‐fat functionality to liquid oils include the addition of unusual compounds to oil, leading to its gelation. These include small‐molecule organogelators such as phytosterols and 12‐hydroxystearic acid, which self‐assemble into crystalline fibers which trap oil. Other crystalline additives include waxes, ceramides, monoacylglycerides, and other surfactants. Recently, the polymer ethyl cellulose was reported to form a polymer gel in triacylglyceride (TAG) oils. Other non‐conventional strategies also include the formation of protein‐stabilized cellular solids with oil trapped within the cells. In this review, we summarize the research on each one of these components in order to provide a comprehensive overview of the state of the area in oleogel research and provide future perspectives.

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.

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 score0.055
Threshold uncertainty score0.207

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.017
GPT teacher head0.265
Teacher spread0.248 · 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