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A SMALL SCALE LABORATORY NOODLE SHEETING MACHINE<sup>1</sup>

2002· article· en· W2093323225 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.

Bibliographic record

VenueJournal of Texture Studies · 2002
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsCanadian International Grains InstituteAgriculture and Agri-Food Canada
Fundersnot available
KeywordsFood scienceRheologyMathematicsViscoelasticityMaterials scienceComposite materialChemistry

Abstract

fetched live from OpenAlex

ABSTRACT Rheological properties of cooked noodles made on a small‐scale laboratory sheeting machine developed at the Cereal Research Centre were strongly correlated with the same properties of noodles made on a pilot noodle sheeting machine. Values obtained from both machines were evaluated against 22 laboratory tests used in our breeding program. For example, cooked noodle viscoelasticity (r=0.89, P &lt; 0.01) and cooked noodle cutting force (r = 0.79, P &lt; 0.01) were similar. Cooked noodle viscoelasticity of fresh noodles made on the laboratory machine correlated significantly (P &lt; 0.05) with 17 of the tests and cooked noodle cutting force correlated significantly with 18 tests. Cooked noodle viscoelasticity of dried noodles made with the pilot plant noodle machine had significant correlations (P &lt; 0.05) with only 14 of the tests and cooked noodle cutting force had significant correlations with 15 tests. The pilot plant machine requires a minimum of 500 g for one sample, while the small‐scale sheeting machine requires only 5‐10 g of flour.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.523

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 head0.040
GPT teacher head0.264
Teacher spread0.224 · 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