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Record W3206641329 · doi:10.1093/jas/skab235.816

PSXIII-1 Effect of varieties and processing methods on physicochemical, nutritional, molecular structural characteristics of feed chickpeas

2021· article· en· W3206641329 on OpenAlexaff
Linda Cerna, Peng Yu

Bibliographic record

VenueJournal of Animal Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotanical Research and Chemistry
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRumenFood scienceIncubationRuminantCarbohydrateChemistryDigestion (alchemy)BiologyAgronomyBiochemistryChromatography

Abstract

fetched live from OpenAlex

Abstract The objectives of this study were to evaluate the effect of varieties and heat processing methods on molecular structural, physicochemical, and nutritional characterization of feed chickpeas; evaluate the effect of heat processing methods, dry heat, wet heat and microwave irradiation processing method on feed chickpeas as an alternative source for protein and energy feed for ruminant livestock. To reveal the molecular structure spectral profile of chickpeas varieties and the molecular structure changes when applied heat processing methods, vibrational molecular spectroscopy was applied. Feed chickpea samples were determined for chemical profile, energy values, carbohydrate fractions. Subsequently, chickpea samples were incubated in the rumen of dairy cows for degradation kinetics analysis of nutrients. The intestinal digestion of feed chickpea samples was determined using three-step in vitro method with pre-incubation at 16h. Later, protein and carbohydrate related molecular spectral features before and after incubation were performed using vibrational ATR-FTIR molecular spectroscopy. The interactive relationship between processing induced molecular spectral profile changes and nutrient metabolism and availability were studied. The available results showed that varieties and heat processing methods significantly impacted molecular structural, physicochemical, and nutritional characterization of feed chickpeas.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.078
Threshold uncertainty score0.198

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.016
GPT teacher head0.322
Teacher spread0.306 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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