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Record W3025806663 · doi:10.3390/dairy1010005

Ketosis an Old Story Under a New Approach

2020· article· en· W3025806663 on OpenAlexafffund
Guanshi Zhang, Burim N. Ametaj

Bibliographic record

VenueDairy · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsUniversity of Alberta
FundersGenome AlbertaAlberta Livestock and Meat Agency
KeywordsKetosisMedicineKetone bodiesIce calvingEndocrinologyLactationPregnancyBiologyDiabetes mellitus

Abstract

fetched live from OpenAlex

Ketosis, characterized by high concentrations of ketone bodies in the blood, urine, and milk, affects a considerable number of cows immediately after calving. Although much is known about ketosis, dairy cows continue to be affected in every herd world-wide. Cows affected by ketosis are treated with palliative treatments after the disease is diagnosed. This is a very expensive approach and costs the dairy industry extra expenses, contributing to lower profitability of dairy herds. In this review article, we summarize the mainstream view on ketosis, classification of ketosis into three types, current diagnostic approaches to ketosis, and the economic impact of ketosis on dairy farms. Additionally, we discuss the most recent applications of the new ‘omics’ science of metabolomics in studying the etiopathology of ketosis as well as its contribution in identification of novel screening or diagnostic biomarkers of ketosis.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.451

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 head0.032
GPT teacher head0.246
Teacher spread0.214 · 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 designNot applicable
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

Citations59
Published2020
Admission routes2
Has abstractyes

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