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Record W2972991158 · doi:10.3920/978-90-8686-891-9_137

Amino acid digestibility of canola meal estimated with pulse-dose in dairy cows or in roosters

2019· article· en· W2972991158 on OpenAlexaff
K. Békri, A. Roussi, H. Lapierre, D. Pellerin, D.R. Ouellet

Bibliographic record

VenueEnergy and protein metabolism and nutrition · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food CanadaUniversité Laval
Fundersnot available
KeywordsCanolaMealAmino acidAnimal scienceFood scienceChemistryBiologyBiochemistry

Abstract

fetched live from OpenAlex

Intestinal digestibility of ruminally undegraded feed protein (RUP) is needed to formulate dairy rations adequately. Thus, our objective was to compare estimates of amino acid (AA) intestinal digestibility of canola meal rumen residues (CMr) using a pulse-dose in dairy cows or caecectomised roosters. Four cows received a pulse-dose of 40 g 15N-labelled CMr (15N-CMr) into the duodenum. The faeces were collected continuously for 28 hours. The digestibility was estimated from cumulative fecal recovery of 15N-AA. In parallel, caecectomised roosters were used to determine the intestinal digestibility of 15N-CMr. All digestibility coefficients of essential AA estimated with the pulsedose method were lower than those estimated with the roosters. The digestibility coefficients from the pulse-dose method were lower than the 75% used by NRC (2001) for the RUP of CM. The digestibility coefficients of AA obtained with the roosters averaged between 76 and 87%. Reduction of the estimation of metabolizable protein (MP) supply when CM substitutes a protein source is not due to the low RUP digestibility used by NRC.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.222
Teacher spread0.206 · 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 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
Published2019
Admission routes1
Has abstractyes

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