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Complex B Vitamin Improves Performance of Mediterranean Buffalo Calves in Artificial Suckling from Birth to Weaning

2013· article· en· W2113650754 on OpenAlexvenueno aff
Lenita Camargo Verdurico, Jefferson Rodrigues Gandra, José Esler de Freitas Júnior, Rafael Villela Barletta, Caio S. Takiya, Flávio Garcia Vilela, Gustavo Delfino Calomeni, R. Gardinal, Paulo César Nunes Gil, Francisco Palma Rennó

Bibliographic record

VenueJournal of Buffalo Science · 2013
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsnot available
Fundersnot available
KeywordsWeaningAlbuminAnimal scienceBiologyVitaminBody weightGlobulinEndocrinology

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate the ponderal development, blood parameters and hematological from buffalo calves in artificial suckling supplemented with vitamin-mineral additive from birth to weaning. 18 buffalo calves were used and divided into two random groups: 1) control group; 2) group supplemented with vitamin B complex (Metacell ®). To evaluation of the ponderal development, the calves were weighed weekly and measured the thoracic perimeter, height and body length. Blood samples were collected weekly throughout the experimental period by jugular vein puncture. The group of calves supplemented with additives showed the greatest ponderal development in relation to the control group. Similarly the group of calves supplemented with additive has higher concentration of total cholesterol, urea and total protein. However there was no additive effect on the levels of glucose, albumin and β-hydroxybutyrate. The values of leukocytes, neutrophils, eosinophils, lymphocytes increased with the oral use of the additive in relation to the control group. This study supports the definition of reference values for the evaluation of buffalo calves during suckling period. The additive use to buffalo calves improves ponderal performance without significantly alter the metabolic profile.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.074
GPT teacher head0.336
Teacher spread0.262 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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