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Record W7062561577

Use of an air-coupled ultrasound technique to assess the mechanical properties of white salted noodle dough and its potential capability in prediction of cooked noodle texture

2018· dissertation· en· W7062561577 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMspace (University of Manitoba) · 2018
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNucleofectionDiafiltrationGestational periodArticular cartilage damageLiquationFusible alloy
DOInot available

Abstract

fetched live from OpenAlex

In this study, an innovative technique—air-coupled ultrasound—was used to measure the mechanical properties of white salted noodles, in a fast, non-destructive, non-contact way. The ultrasound technique was sensitive to the changes brought about by dough moisture content, work input (either from the mixing or sheeting process), as well as the changes in noodle properties with time. In addition, the cooked noodle texture was assessed by conventional methods: an instrumental method and a trained sensory panel. Noodles were less firm with increased water content, and with prolonged storage time (24 hours). Noodles made with CWRW (Canada Western Red Winter) flour had a comparable firmness as noodles made from high protein content flour (Canada Western Red Spring). Overall, the cooked noodle texture was highly correlated with the dough properties measured with the ultrasound technique. Therefore, this research suggests that air-coupled ultrasound has a promising capability for the prediction of noodle quality.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.991

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 head0.025
GPT teacher head0.214
Teacher spread0.189 · 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