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Record W2146971814 · doi:10.3382/ps.2012-02332

Factors that affect the nutritive value of canola meal for poultry

2012· review· en· W2146971814 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePoultry Science · 2012
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCanolaMealFood scienceValue (mathematics)Affect (linguistics)Animal scienceBiologyMathematicsChemistryAgronomyStatistics

Abstract

fetched live from OpenAlex

This article reviews the factors affecting the nutritive value of canola meal (CM), including glucosinolates, sinapine, phytic acid, tannins, dietary fiber, and electrolyte balance. It also addresses the means of improving the nutritive value of CM throughout seed dehulling, development of low-fiber canola, or application of feed enzymes. Over the years, the glucosinolate content of canola has been declining steadily and is now only about one-twelfth of that of the older high-glucosinolate rapeseed (that is, 10 vs. 120 μmol/g). Therefore, the rations for broilers or laying hens could now contain 20% of CM without producing any adverse effects. Tannins are of lesser importance due to their presence in the hull fraction and thus low water solubility. Sinapine has been implicated with the production of a "fishy" taint in brown-shelled eggs, which results from a genetic defect among the strain of Rhode Island Red laying hens. The White Leghorns have been reported not to be affected. Although lower in protein, CM compares favorably with soybean meal with regard to amino acid content. Because CM contains more methionine and cysteine but less lysine, both meals tend to complement each other when used together in poultry diets. Canola meal is low in arginine (Arg) which could be of importance when introducing CM to broiler diets at high inclusion rates. The Arg content of CM is approximately two-thirds of that of soybean meal. Chickens fail to synthesize Arg and are highly dependent on dietary sources for this amino acid. Supplementation of Arg to CM-based diets has been shown to partly restore the growth performance. Dietary cation-anion difference in CM is also less than optimal due to the high sulfur and low potassium contents. Seed dehulling has not been very successful due to excessive fineness and thus difficulties with percolation of the miscella through the cake. Development of low-fiber, yellow-seeded canola and the use of enzymes have proven to increase the energy utilization and the nutritive value of CM for 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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.209
GPT teacher head0.351
Teacher spread0.143 · 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