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Record W1963972858 · doi:10.3168/jds.2013-7125

The effect of feeding canola meal on concentrations of plasma amino acids

2014· review· en· W1963972858 on OpenAlexafffund
R. Martineau, D.R. Ouellet, H. Lapierre

Bibliographic record

VenueJournal of Dairy Science · 2014
Typereview
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaCanola Council of Canada
KeywordsCanolaMealAnimal scienceChemistryUreaPlasma concentrationBlood plasmaFood scienceInternal medicineEndocrinologyBiologyBiochemistryMedicine

Abstract

fetched live from OpenAlex

An initial meta-analysis on isonitrogenous experiments where a protein source was replaced by canola meal (CM) showed that CM feeding increased yields of milk and milk protein and apparent N efficiency. The objective of the current study was to determine if these responses were related to increased changes in plasma AA concentrations. Although only half of the experiments of the initial meta-analysis reported concentrations of plasma AA and could be used in the current meta-analysis, lactational responses to CM feeding were similar to those reported previously. In the current meta-analysis, CM feeding increased plasma concentrations of total AA, total essential AA (EAA) and all individual EAA, but decreased concentrations of blood and milk urea-N. The current meta-analysis suggests that CM feeding increased the absorption of EAA, which would be responsible for the increased milk protein secretion and the increased apparent N efficiency.

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)0.007
metaresearch head score (Gemma)0.014
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 score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.013
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.046
GPT teacher head0.385
Teacher spread0.339 · 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

Citations29
Published2014
Admission routes2
Has abstractyes

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