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Record W2766104847 · doi:10.1094/cfw-62-5-0227

AACCI Approved Methods Technical Committee Report: Collaborative Study on a Method for Determining the Water Holding Capacity of Pulse Flours and Their Protein Materials (AACCI Method 56-37.01)

2017· article· en· W2766104847 on OpenAlexaff
N. Wang, Jennifer A. Wood, Joe Panozzo, G. C. Argansoa, Clifford Hall, L. Chen, Mukti Singh, M.T. Nickerson

Bibliographic record

VenueCereal Foods World · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsUniversity of AlbertaUniversity of SaskatchewanCanadian International Grains Institute
Fundersnot available
KeywordsWater holding capacityPulse (music)ChromatographyChemistryComputer scienceFood scienceTelecommunications

Abstract

fetched live from OpenAlex

A method for determining the water holding capacity of pulse flours and their protein materials has been developed and subjected to an interlaboratory study. Eleven participants analyzed twelve blind duplicates of six different samples in a collaborative study to evaluate the repeatability and reproducibility of the method. Statistical analysis of the collaborative data determined that the within-laboratory repeatability standard deviation (sr) ranged from 0.018 to 0.083, and the among-laboratory reproducibility standard deviation (sR) ranged from 0.039 to 0.18. The within-laboratory relative standard deviation (RSDr) of samples ranged from 2.08 to 5.49%, and the among-laboratory relative standard deviation (RSDR) ranged from 4.99 to 7.49%. Results indicated that the method has good repeatability and reproducibility.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.054
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.091
Threshold uncertainty score0.306

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.055
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.002
Science and technology studies0.0040.002
Scholarly communication0.0030.001
Open science0.0050.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0910.069

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 head0.059
GPT teacher head0.402
Teacher spread0.343 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2017
Admission routes1
Has abstractyes

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