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Record W4388153840 · doi:10.3168/jds.2023-23972

Fat composition of milk replacer influences postprandial and oxidative metabolisms in dairy calves fed twice daily

2023· article· en· W4388153840 on OpenAlexaff
J.N. Wilms, Mohtasham Ghaffari, H. Sauerwein, M.A. Steele, J. Martín‐Tereso, L.N. Leal

Bibliographic record

VenueJournal of Dairy Science · 2023
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsUniversity of Guelph
FundersRheinische Friedrich-Wilhelms-Universität Bonn
KeywordsFood sciencePostprandialRapeseedComposition (language)StrawStarterLactoseTriglycerideChemistryAnimal scienceAnimal fatBiologyBiotechnologyCholesterolBiochemistry

Abstract

fetched live from OpenAlex

Milk replacers (MR) for calves contain alternative fat sources as substitute for milk fat. This substitution leads to differences in fat properties, such as the fatty acid profile and the triglyceride structure. This study evaluated how fat composition in MR affects gastrointestinal health, blood redox parameters, and postprandial metabolism in calves fed twice daily. Forty-five individually housed male Holstein-Friesian calves (2.3 ± 0.85 d of age) were assigned to 1 of 15 blocks based on the age and the day of arrival. Within each block, calves were randomly assigned to 1 of 3 experimental diets and received their respective diet from arrival until 35 d after arrival. The 3 experimental diets (n = 15 per treatment group) consisted of an MR with a blend of vegetable fats containing rapeseed and coconut (VG), an MR with only animal fats from lard and dairy cream (AN), and an MR containing a mixture of animal and vegetable fats including lard and coconut (MX). The fatty acid profile of each MR was formulated to resemble that of bovine milk fat while using only 2 fat sources. All MR were isoenergetic, with 30% fat (% DM), 24% crude protein, and 36% lactose. Chopped straw and water were available ad libitum from arrival onward but no starter feed was provided. Daily milk allowances were 6.0 L from d 1 to 5, 7.0 L from d 6 to 9, and 8.0 L from d 10 to 35, divided into 2 equal meals and prepared at 135 g/L (13.5% solids). Fecal appearance was scored daily; calves were weighed and blood was drawn on arrival and weekly thereafter. Urine and feces were collected over a 24-h period at wk 3 and 5 to determine apparent total-tract digestibility and assess gastrointestinal permeability using indigestible markers. Postprandial metabolism was evaluated at wk 4 by sequential blood sampling over 7.5 h, and the abomasal emptying rate was determined by acetaminophen appearance in blood. Fat composition in MR did not affect growth, MR intake, gastrointestinal permeability, nor nutrient digestibility. The percentage of calves with abnormal fecal scores was lower at wk 2 after arrival in calves fed VG than MX, whereas AN did not differ from the other treatments. Calves fed AN and MX had higher thiobarbituric acid reactive substances measured in serum than VG, whereas plasma ferric-reducing ability was greater in calves fed MX than VG. Postprandial acetaminophen concentrations did not differ across treatment groups, but the area under the curve was smaller in calves fed VG than in the other 2 treatments, which is indicative of a slower abomasal emptying. Postprandial serum triglyceride concentration was greater in calves fed AN than VG, whereas MX did not differ from the other treatments. Based on these outcomes, all 3 fat blends can be considered suitable for inclusion in MR for calves.

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)0.000
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.358
Teacher spread0.310 · 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

Citations18
Published2023
Admission routes1
Has abstractyes

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