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Record W2365522316

Method of two step-heating on tofu's physical properties

2007· article· en· W2365522316 on OpenAlexaff
Yi Liu

Bibliographic record

VenueFood Science and Technology International · 2007
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsScience North
Fundersnot available
KeywordsSyneresisDenaturation (fissile materials)ChemistryYield (engineering)Soy proteinFood scienceChemical engineeringChromatographyMaterials scienceComposite materialNuclear chemistry
DOInot available

Abstract

fetched live from OpenAlex

Thermal denaturation of soy proteins is a pre-requisite for tofu-gel formation. The two main proteins in soy proteins are glycinin of which denaturation temperature is 92℃ and β-conglycinin of which denaturation temperature is 71℃. Soymilk was heated at 75℃ and 95℃ for different time(5min, 7min, 10min). Comparing to one step heating, selective thermal denaturation has promoted some textural parameters, syneresis and yield. Meanwhile, the effects of tofu’s syneresis and yield which are under the method of two-step heating are much better than those of one-step heating. However, there are not direct relationships on tofu's quality with protein content of different samples and the heating methods.

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 score0.392
Threshold uncertainty score0.235

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 studies0.0000.001
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 head0.032
GPT teacher head0.330
Teacher spread0.298 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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