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Record W2588302336 · doi:10.5539/jas.v9n3p90

Aspects of Acidosis in Ruminants with a Focus on Nutrition: A Review

2017· review· en· W2588302336 on OpenAlexvenueno aff
Tiago Neves Pereira Valente, Cláudia Batista Sampaio, Érico da Silva Lima, Bruno Borges Deminicis, Andréia Santos Cezário, Wallacy Barbacena Rosa dos Santos

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsRumenAcidosisDry matterAnimal scienceNEFAFeedlotNeutral Detergent FiberLactic acidOsmotic concentrationFood scienceChemistryBiologyBiochemistryFatty acidFermentationEndocrinologyBacteria

Abstract

fetched live from OpenAlex

An increased risk of acidosis in animals is associated with a high dry matter intake (DMI), which in turn results in the consumption of more fermentable organic matter (OM) in the rumen leading to a high production of volatile fatty acids (VFA). This is observed in lactating dairy cows and animals in a feedlot. Acute acidosis occurs when there is a severe drop in the pH of the rumen. A prolonged period when pH of in rumen remains low, it leads to sub-acute ruminal acidosis (SARA), which is a temporary imbalance between acid production and absorption. An associated change of an acute increase in the ruminal osmolarity and the accumulation of glucose and lactate in its stereoisomeric forms (D-lactate and L-lactate), is observed in the rumen fluid. However, in the sub-acute form, the accumulation of lactic acid occurs in the rumen. To a great extent, these changes in the rumen are due to high concentrations of VFA. The best way to avoid problems with ruminal acidosis is an adequate supply of neutral detergent fiber (NDF) in the diet, preferentially with large particle size and length to stimulate rumination and consequently greater buffering efficiency, thus maintaining the balance between pH and microorganisms in the rumen.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.072
GPT teacher head0.326
Teacher spread0.253 · 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 designOther design
Domainnot available
GenreReview

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

Citations33
Published2017
Admission routes1
Has abstractyes

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