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Record W3131314820 · doi:10.3168/jds.2020-19405

Effects of energy source in milk replacer on glucose metabolism of neonatal dairy calves

2021· article· en· W3131314820 on OpenAlexaff
A.C. Welboren, B. Hatew, Ó. López-Campos, J.P. Cant, L.N. Leal, J. Martín‐Tereso, M.A. Steele

Bibliographic record

VenueJournal of Dairy Science · 2021
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of AlbertaUniversity of Guelph
Fundersnot available
KeywordsFood scienceEnergy metabolismDairy cattleAnimal scienceDairy industryChemistryCarbohydrate metabolismBiologyBiochemistryEndocrinology

Abstract

fetched live from OpenAlex

) with MR and measuring its appearance within plasma. Sequential blood samples were collected and analyzed for plasma glucose, insulin, and acetaminophen concentrations. Calves were killed on d 7 and liver and pancreatic tissues were collected for histomorphological analysis. Partially replacing lactose in MR with fat increased body weight gain (679.6 vs. 462.0 ± 69.68 g/d) and gain:metabolizable energy intake (186.6 vs. 120.5 ± 20.21 g of body weight/Mcal) compared with feeding HL MR. Furthermore, abomasal emptying rate was reduced in calves fed HF MR. Calculated variables based on postprandial glucose and insulin concentrations, such as maximum concentration or area under the curve, were or tended to be lower in HF than in HL calves. Postprandial insulin sensitivity tended to be reduced in HL compared with HF, although insulin sensitivity estimated from the insulin-modified intravenous glucose tolerance test was not different. Calves fed HF MR showed enhanced glucose-stimulated insulin secretion. In conclusion, partially replacing lactose in MR with fat resulted in smaller fluctuations in postprandial glucose and insulin concentrations and tended to increase postprandial but not fasting insulin sensitivity in neonatal dairy 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.033
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.276
Teacher spread0.266 · 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 teacher head, 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

Citations24
Published2021
Admission routes1
Has abstractyes

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