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Record W2605468875 · doi:10.1139/cjas-2016-0229

Fractionation of canola meal using sieving technology

2017· article· en· W2605468875 on OpenAlexaffvenue
Gustavo A Mejicanos, Anna Rogiewicz, C. M. Nyachoti, B.A. Slominski

Bibliographic record

VenueCanadian Journal of Animal Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCanolaBrassicaMealFractionationSoybean mealChemistryBroilerFood scienceAnimal scienceBiologyAgronomyChromatography

Abstract

fetched live from OpenAlex

The present study explored the potential for the production of canola meal (CM) fractions of different chemical and nutritive composition. Three meals from conventional black- and yellow-seeded Brassica napus and Brassica juncea canola were subjected to sieving. The use of sieves from 250 to 600 µm resulted in the production of fractions fine 1, fine 2, medium, and coarse. When compared with the parent meals, the content of total dietary fiber of fractions fine 1 and 2 decreased from 300 to 214 and 267 g kg−1 for conventional CM, 270 to 216 and 234 g kg−1 for B. napus yellow, and 255 to 153 and 187 g kg−1 for B. juncea meal. Crude protein increased from 368 to 420 and 396 g kg−1 for conventional CM, 410 to 436 and 430 g kg−1 for yellow CM, and 423 to 479 and 468 g kg−1 for B. juncea meal. The effects of three parent meals and their respective fractions fine 1 and 2 were evaluated in a growth performance experiment with broiler chickens. There was no significant effect of CM diets on growth performance indicating that CM and its low-fiber fractions could effectively replace soybean meal in diets for young poultry.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.256
Teacher spread0.222 · 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

Citations13
Published2017
Admission routes2
Has abstractyes

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