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Record W2078790448 · doi:10.1094/cchem-10-13-0206-cesi

Ultrasonic Characterization of Unyeasted Bread Dough of Different Sodium Chloride Concentrations

2014· article· en· W2078790448 on OpenAlexafffund
Filiz Köksel, Anatoliy Strybulevych, J. H. Page, Martin G. Scanlon

Bibliographic record

VenueCereal Chemistry · 2014
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsChemistryBubbleSodiumAttenuationVolume fractionAnalytical Chemistry (journal)RADIUSUltrasonic sensorVolume (thermodynamics)ChromatographyThermodynamicsMechanicsOptics

Abstract

fetched live from OpenAlex

ABSTRACT Altering the properties of dough by reducing sodium chloride (NaCl) content affects aeration processes during mixing. The effect of NaCl concentration on the bubble size distribution (BSD) in unyeasted doughs was investigated by an ultrasonic transmission technique through analysis of frequency‐dependent ultrasonic phase velocity and attenuation coefficient. As NaCl concentration was decreased, the volume fraction of gas in the dough increased, resulting in a larger attenuation coefficient for the dough. From the peak in attenuation coefficient, estimates of the median radius and the width of the lognormal BSD in the dough were extracted, both of which were sensitive to the dough's NaCl concentration. As NaCl concentration was reduced, the bubble radius decreased and the width of the distribution increased, in accordance with expectations arising from changes in the dough's consistency. Over the course of 150 min, the radius increased (40–50%) and the width decreased (4–8%) for all dough formulations, consistent with changes in the BSD arising from disproportionation. These dynamic changes demonstrate that dough is an interesting soft material whose formulation can be manipulated to enable it to possess different BSDs; the diffusively driven evolution in these bubble sizes can be investigated noninvasively with ultrasound.

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 score0.009
Threshold uncertainty score0.370

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 studies0.0000.000
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 head0.216
Teacher spread0.206 · 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

Citations13
Published2014
Admission routes2
Has abstractyes

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