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Record W2283938616 · doi:10.1186/s40781-016-0085-5

Recent advances in canola meal utilization in swine nutrition

2016· review· en· W2283938616 on OpenAlexaff
Gustavo A Mejicanos, N. Sanjayan, In Ho Kim, C. M. Nyachoti

Bibliographic record

VenueJournal of Animal Science and Technology · 2016
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCanolaMealBiologyFood scienceBiotechnology

Abstract

fetched live from OpenAlex

Canola meal is derived from the crushing of canola seed for oil extraction. Although it has been used in swine diets for a long time, its inclusion levels have been limited due to concerns regarding its nutritive value primarily arising from results of early studies showing negative effects of dietary canola meal inclusion in swine diets. Such effects were attributable to the presence of anti-nutritional factors (ANF; notably glucosinolates) in canola meal. However, due to advances in genetic improvements of canola that have led to production of cultivars with significantly lower ANF content and improved processing procedures, canola meal with a superior nutritive value for non-ruminant animals is now available. Therefore, the aim of this paper is to review the recent studies in the use of canola meal as feedstuff for swine, the factors influencing its use and the strategies to overcome them. First a historical overview of the development of canola is provided.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.053
GPT teacher head0.327
Teacher spread0.274 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations108
Published2016
Admission routes1
Has abstractyes

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