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Nanostructured Fat Crystal Systems

2014· review· en· W2116358125 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnnual Review of Food Science and Technology · 2014
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaAdvanced Foods and Materials Network
KeywordsNanocrystalline materialNanostructureNanotechnologyMaterials scienceNanoengineeringCrystalliteCrystallizationNanoscopic scaleMicrometerPorosityNanocrystalSupramolecular chemistryChemical engineeringCrystal structureChemistryCrystallographyComposite materialPhysics

Abstract

fetched live from OpenAlex

A new understanding of the nature and organization of fat crystalline supramolecular structure, in particular at the nanoscale, has arisen in the past three years. These new findings have helped establish that the first step in the formation of a triacylglycerol network is the creation of nanocrystalline platelets that aggregate into polycrystalline clusters in the micrometer range, ultimately forming a three-dimensional network. This review explains how fat nanostructure can be characterized and highlights recent findings on how crystallization parameters influence the formation of fat nanocrystals. For instance, shear has been shown to modify not only nanoplatelet size but also their aggregation, affecting some macroscopic properties such as porosity and, therefore, the ability of the network to effectively bind liquid oil. This new information on fat nanostructure is relevant from scientific and technological standpoints and has opened up the possibility of nanoengineering material properties as well as developing new products and processes.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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