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Record W3185862984

Empirical modelling of vitamin B12 duodenal flow in lactating dairy cows

2020· article· en· W3185862984 on OpenAlexaff
V. Brisson, C.L. Girard, J.A. Metcalf, D.S. Castagnino, J. Dijkstra, J.L. Ellis

Bibliographic record

VenueSocio-Environmental Systems Modeling · 2020
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
Fundersnot available
KeywordsVitamin B12Dairy cattleAnimal scienceCyanocobalaminFood scienceChemistryBiologyBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

Unlike other B vitamins, vitamin B12 is not found in plants and is produced only by bacteria. Therefore, supply to the dairy cow, unless provided via supplementation, will mainly be the result of B12 manufactured by ruminal microbes. The duodenal flow of B vitamins therefore represents the amount of vitamin available for absorption by the ruminant, which can be used for essential metabolic functions and milk production. However, diet composition may affect ruminal synthesis and the resulting duodenal flow (DF) of vitamin B12, due to alterations to fermentation and ruminal conditions. Therefore, the objective of this study was to conduct a meta-analysis describing how diet composition affects DF of vitamin B12. Data were collected from 340 individual lactating cows involved in 16 published studies. Saved diet and duodenal samples from these studies were subsequently reanalyzed for B vitamin content to create the database used in the present study. Potential driving variables considered included (DM basis) dietary organic matter (%), NDF (%), starch (%), crude protein (%) and DMI (kg/d). The meta-analysis was conducted in 3 steps, followed by statistical evaluation of the resulting empirical models. A Spearman correlation matrix was constructed between all potential driving variables to assess for collinearity between X variables, and guide model creation. Then, using Cook's distance statistic (Proc MIXED), outliers were determined and removed. Finally, a suite of potential models (with study treated as a random effect) were developed in GLIMMIX. Where models were statistically significant, evaluation was completed using root mean square prediction error (RMSPE) and concordance correlation coefficient (CCC) to determine the sources of error. The best performing model was: B12DF (mg/d) = −7.87 (�2.46) + 0.29 (�0.056) � DietNDF(%) + 0.44 (�0.042) � DMI (kg/d); RMSPE: 41.1%, CCC: 0.268. In conclusion, DF of B12 was positively impacted by both the overall DMI and the dietary NDF content of the diet. This information may be used to better understand supply of vitamin B12 to the modern dairy cow, in relation to requirements, to improve milk production 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)0.020
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.009
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0030.001
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 head0.133
GPT teacher head0.317
Teacher spread0.183 · 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 designSimulation or modeling
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

Citations2
Published2020
Admission routes1
Has abstractyes

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