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Record W4400773483 · doi:10.3390/vetsci11070324

Reproductive Tract Microbiota of Mares

2024· review· en· W4400773483 on OpenAlexaff
Ana Gil-Miranda, Jennifer L. MacNicol, Daniela Orellana-Guerrero, Juan C. Samper, Diego E. Gómez

Bibliographic record

VenueVeterinary Sciences · 2024
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDysbiosisBiologyReproductive tractInfertilityVaginaEstrous cyclePhysiologyEndometritisMicrobiomeReproductive healthFertilityFemale Reproductive TractDiseaseGut floraUterusImmunologyMedicineBioinformaticsPregnancyInternal medicinePopulationGeneticsEnvironmental healthEndocrinology

Abstract

fetched live from OpenAlex

The female reproductive tract microbiota is a complex community of microorganisms that might be crucial in maintaining a healthy reproductive environment. Imbalances in the bacterial community (dysbiosis) and the reduction of beneficial organisms and pathogen proliferation are associated with disease. Endometritis is a common cause of fertility problems in mares, and it is still challenging to diagnose and treat based on routine culture results of certain microorganisms. Although high-throughput sequencing studies provide helpful information regarding the composition of the reproductive tract microbiota in mares, there are still challenges in defining a "normal" microbiota. The primary objective of this literature review is to summarize the current knowledge regarding the microbiota present in the reproductive tract of mares, including the vagina, cervix, and uterus. The second objective is to describe the relevant factors that can impact the reproductive microbiota of mares, including the estrous cycle stage, the type of species (genera) investigated, season, and geographic location. The rationality of identifying the normal microbiota in the reproductive tract of a mare will likely aid in understanding the impact of the microbiota on the host's reproductive health and contribute to the treatment and prevention of equine sub and infertility issues.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.158
GPT teacher head0.362
Teacher spread0.204 · 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
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

Citations12
Published2024
Admission routes1
Has abstractyes

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