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Record W4396787799 · doi:10.3168/jdsc.2023-0535

A comprehensive integration of factors affecting vitamin B12 concentration in milk of Holstein cows: Genetic variability, milk productivity, animal characteristics, and feeding management

2024· article· en· W4396787799 on OpenAlexaffabout
M. Duplessis, C.L. Girard, D. Pellerin, Liliana Fadul-Pacheco, R.I. Cue

Bibliographic record

VenueJDS Communications · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsMcGill UniversitySte. Anne's HospitalUniversité LavalUniversité de SherbrookeValacta (Canada)Agriculture and Agri-Food Canada
Fundersnot available
KeywordsBiologyAnimal scienceLactationHeritabilityFood scienceSireDry matterVitamin B12Dairy cattleGenetic variationBiochemistryPregnancyGenetics

Abstract

fetched live from OpenAlex

Daily vitamin B 12 (VB 12 ) requirements of humans can naturally be fulfilled by animal product consumption, especially products from ruminants because of bacteria dwelling in their rumen. Indeed, only bacteria can synthesize this vitamin. Milk is hence an excellent source of VB 12 . This cross-sectional study was undertaken to unravel factors, such as genetic variation, diet and cow characteristics, and milk production, explaining the large variation in milk B 12 concentration among cows by using an integrative approach. Milk samples from 2 consecutive milkings were collected from 3,533 Canadian Holstein cows (1,239 first, 932 s, and 1,362 third and more lactations) located in 99 herds with various feeding management. For genetic variation analysis purpose, pedigrees were traced back for 3 complete generations for each sire and dam. A total of 10,021 identities were used in the subsequent genetic analyses. Milk VB 12 averaged 4.2 ng/mL with a range between 0.7 and 9.0 ng/mL. Dietary fiber (NDF from forage, dietary NDF, ADF, and lignin) increased and dietary components related to energy (non-fiber carbohydrate, starch, net energy of lactation, and percentage of concentrate) decreased VB 12 in milk. Milk VB 12 varied with days in milk, with a similar pattern as milk fat and protein concentration lactation curves. Milk VB 12 increased as age at calving increased. When disregarding the herd variance, heritability value was 0.37, meaning that milk VB 12 can be modified by genetic selection. The final model including factors related to the diet, animal characteristics and milk productivity, and genetic variation explained 79% (pseudo-R 2 ) of the milk VB 12 variation. When excluding the random effect of the cow, i.e., excluding the animal and genetic relationships, the pseudo-R 2 dropped to 43%, reinforcing the importance of genetic variation in explaining milk VB 12 variation. To our knowledge, the present study is the most comprehensive evaluation of factors affecting milk VB 12 variation including the greatest number of cows from various lactation stages.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.418

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.280
Teacher spread0.256 · 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 designObservational
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
Published2024
Admission routes2
Has abstractyes

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