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Record W2603127690

Glutograph 'E': A simple rheometer to measure quality of cooked pasta

2016· dissertation· en· W2603127690 on OpenAlexaboutno aff
Kueh Fei Fernandez

Bibliographic record

VenueNDSU Repository (North Dakota State University) · 2016
Typedissertation
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsnot available
Fundersnot available
KeywordsTexture (cosmology)RheometerMathematicsMaterials scienceFood scienceComposite materialArtificial intelligenceRheologyChemistryComputer science
DOInot available

Abstract

fetched live from OpenAlex

Glutograph ?E? was designed to determine the stretch and relaxation properties of wet gluten. Usage of this equipment to evaluate the cooked pasta texture has not been reported and thus is the goal of this research. Procedure development involved the evaluation of the number of impulses, relaxation time, the number of spaghetti strands, and the number of tests per sample to give a reliable treatment mean. Different impulses settings affected the magnitude of strain, time and percent recovery. Traditional spaghetti required less time to reach 800 B.U and had greater percent recovery and strain values than did nontraditional spaghetti. Glutograph results were compared with firmness results obtained using a texture analyzer (TA-XT2) with a pasta blade or with a Modified Ottawa Cell. Compression type probe parameters were positively correlated to glutograph stretch time. Glutograph E can be used as potential equipment to detect differences in texture of cooked pasta.

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 categoriesMeta-epidemiology (narrow)
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 score0.536
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.022
GPT teacher head0.240
Teacher spread0.218 · 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.

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
Published2016
Admission routes1
Has abstractyes

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