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Record W2747911616 · doi:10.21451/1984-3143-ar1002

Impact and mechanisms of inflammatory diseases on embryonic development and fertility in cattle

2017· article· en· W2747911616 on OpenAlexaff
E.S. Ribeiro, M.R. Carvalho

Bibliographic record

VenueAnimal Reproduction · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsConceptusBiologyPregnancyFertilityDiseaseIce calvingPhysiologyHerdLactationImmunologyFetusInternal medicineMedicineGeneticsPopulationAnimal science

Abstract

fetched live from OpenAlex

Inflammatory diseases are prevalent in cattle and impair fertility. Cows affected by inflammatory disease from parturition to the day before breeding have reduced fertilization of oocytes, reduced survival of zygotes to the morula stage, impaired development to early stages of conceptus elongation, reduced secretion of interferon during the period of pregnancy recognition, altered transcriptome of preimplantation conceptus cells, and increased pregnancy loss. Consequently, these cows have reduced pregnancy and calving per breeding. Reduced oocyte competence is a likely reason for the carryover effects of diseases on developmental biology, but impaired uterine environment is also involved. Effects on pregnancy survival are observed up to 5 months after the diagnosis and treatment of disease, and effects on developmental biology seem to be extended into postnatal life in pregnancies that survive until term. Although the biological mechanism mediating the effects of inflammatory diseases are still not completely understood, control of inflammation during the clinical presentation of diseases seems to alleviate the negative effects on reproductive biology. It is increasingly evident that animal health, not only at the time of breeding or pregnancy development but also in the period preceding breeding, is imperative for optimal reproduction in cattle and should always be considered in herd evaluations and managerial decisions.

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 score0.702
Threshold uncertainty score0.155

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 head0.027
GPT teacher head0.269
Teacher spread0.243 · 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

Citations43
Published2017
Admission routes1
Has abstractyes

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