MétaCan
Menu
Back to cohort
Record W1588516488 · doi:10.1002/9781118448298.ch5

Composition of Processed Dry Beans and Pulses

2012· other· en· W1588516488 on OpenAlexaff
Elham Azarpazhooh, Joyce I. Boye

Bibliographic record

Venuenot available
Typeother
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsFood scienceBlanchingNutrientComposition (language)ChemistryDietary fiberFermentationAntinutrientCooking methodsAntioxidantBiochemistryPhytic acid

Abstract

fetched live from OpenAlex

Dry beans are good sources of protein, carbohydrates, dietary fiber, vitamins, and minerals as well as being low in sodium. Specifically, their high content of soluble dietary fiber which can help to lower blood cholesterol, a main risk factor in cardiovascular disease, makes them promising as health foods. Once considered antinutritional, phenolic compounds in beans, especially pigmented/colored beans have good antioxidant properties. Different processing methods (dehulling, soaking, germination, fermentation, blanching and cooking, extrusion cooking, milling) alter the composition of the macro- and micro-nutrients in beans as well as the functionality of the seeds and their derived ingredients. The present chapter provides a review of the effect of processing on the compositional, nutritional and health properties of dry beans with special focus on the quality characteristics of the beans.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.711
Threshold uncertainty score0.999

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)0.0020.000

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 head0.017
GPT teacher head0.245
Teacher spread0.228 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations38
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicFood composition and propertiesFrench-language works237,207