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Record W2093446881 · doi:10.1094/cchem.2002.79.4.596

Shortened Temperature Program for Application with a Rapid Visco Analyser in Prediction of Noodle Quality in Wheat

2002· article· en· W2093446881 on OpenAlexaff
G. B. Crosbie, P. C. Chiu, Andrew S. Ross

Bibliographic record

VenueCereal Chemistry · 2002
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsDepartment of Environment and Conservation
FundersGrains Research and Development Corporation
KeywordsAnalyserChemistryFood scienceStarchMealChromatography

Abstract

fetched live from OpenAlex

ABSTRACT The use of the Rapid Visco Analyser (RVA) for application in the screening of wheat breeding lines for starch quality and potential noodle quality has been limited by relatively low sample throughput. Current methods generally enable only 20–30 samples to be tested each day. This study sought to develop a more rapid time‐temperature profile that could be applied to whole meal samples. A profile that involved a total analysis time of 7.5 min/sample gave measurements of peak viscosity (PV) and breakdown (BD) on whole meal that were highly correlated with corresponding measurements obtained using a more conventional profile that had been applied to low‐extraction flours. BD and PV were also highly correlated with the total texture score of ramen (Chinese‐style alkaline noodles as manufactured in Japan), but only when 1 m M AgNO 3 was used to eliminate the effects of α‐amylase.

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 score0.040
Threshold uncertainty score0.334

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 head0.024
GPT teacher head0.275
Teacher spread0.251 · 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

Citations15
Published2002
Admission routes1
Has abstractyes

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