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Record W2147575170 · doi:10.5539/ijb.v6n3p85

Major Metabolic Diseases Affecting Cows in Transition Period

2014· article· en· W2147575170 on OpenAlexvenueno aff
Leílson Rocha Bezerra, Cezario B. de Oliveira Neto, Marcos Jácome de Araújo, Ricardo Loiola Edvan, Wagner Dias Coelho de Oliveira, Fabrício Brandão Pereira

Bibliographic record

VenueInternational Journal of Biology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsnot available
Fundersnot available
KeywordsKetosisRumenDry matterFood scienceAnimal scienceLactationEnergy balanceChemistryBiologyEndocrinologyFermentationPregnancyEcologyDiabetes mellitus

Abstract

fetched live from OpenAlex

The aim of this study was to perform a literary review on the main metabolic diseases affecting cows during the transition period. The heat increment promoted by higher energy demand that occurs during the transition period between the end of lactation and early management, combined with low dry matter intake due to fetal growth major and consequent reduced ability of the rumen, make the dairy cow highly susceptible to the metabolic diseases ketosis, milk fever and hepatic lipidosis. The increase in blood concentrations of non-esterifies fatty acids during this transition period appear to be linked to the onset of these disorders and this can be explained by the high energy mobilization because of the negative energy balance. Diets with high energy density during this period are extremely necessary to minimize the effects of negative energy balance. In addition it is recommended to feed the animals with foods smaller particles because the capacity of the rumen is reduced. It also recommends the provision of lipid sources such as protected fat and vegetable oils that do not degrade the ruminal microorganisms and even precursors of glucose, such as propylene glycol or starchy concentrates, and not by lipid.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.141

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.013
GPT teacher head0.264
Teacher spread0.251 · 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 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

Citations14
Published2014
Admission routes1
Has abstractyes

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