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Record W2057895172 · doi:10.1094/cfw-57-5-0230

AACCI Approved Methods Technical Committee Report: Collaborative Study on a Method for Determining Firmness of Cooked Pulses (AACCI Method 56-36.01)

2012· article· en· W2057895172 on OpenAlexaff
N. Wang, Joe Panozzo, Jennifer A. Wood, Linda Malcolmson, Gene Arganosa, Byung‐Kee Baik, D. Driedger, Jixiang Han

Bibliographic record

VenueCereal Foods World · 2012
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Chemometric Analyses
Canadian institutionsUniversity of SaskatchewanAgriculture Food and Rural DevelopmentCanadian International Grains Institute
Fundersnot available
KeywordsChemistryComputer scienceMaterials scienceMathematics

Abstract

fetched live from OpenAlex

A method based on compression through a Kramer shear cell was developed for determining the firmness of cooked pulses. Ten laboratories analyzed twenty-six blind duplicates of thirteen different samples in a collaborative study to evaluate the repeatability and reproducibility of the method. Statistical analysis of the collaborative data indicated that within-laboratory repeatability standard deviation (sr) was in the range of 0.53 to 1.43, and among-laboratory reproducibility standard deviation (sR) varied from 0.74 to 1.94. The within-laboratory relative standard deviation (RSDr) of samples ranged from 2.45 to 7.24%, and the among-laboratory relative standard deviation (RSDR) ranged from 4.23 to 8.80%.

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)0.003
metaresearch head score (Gemma)0.002
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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.438
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.448
Teacher spread0.390 · 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
GenreMethods

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
Published2012
Admission routes1
Has abstractyes

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