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Record W4399360305 · doi:10.1038/s41598-024-63868-1

Dysbiosis not observed in Canadian horses with free fecal liquid (FFL) using 16S rRNA sequencing

2024· article· en· W4399360305 on OpenAlexafffundabout
Robert J. Wester, Lyndsey L. Baillie, Garrett C. McCarthy, Carson C. Keever, Lauren E. Jeffery, Paul Adams

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsAbbotsford Veterinary ClinicKwantlen Polytechnic University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFecesDysbiosis16S ribosomal RNAMedicineBiologyVeterinary medicineZoologyMicrobiologyBioinformaticsGeneticsMicrobiomeBacteria

Abstract

fetched live from OpenAlex

Free Fecal Liquid (FFL), also termed Fecal Water Syndrome (FWS), is an ailment in horses characterized by variable solid and liquid (water) phases at defecation. The liquid phase can be excreted before, during, or after the solid defecation phase. While the underlying causes of FFL are unknown, hindgut dysbiosis is suggested to be associated with FFL. Three European studies investigated dysbiosis in horses with FFL using 16S rRNA sequencing and reported results that conflicted between each other. In the present study, we also used 16S rRNA sequencing to study the fecal microbial composition in 14 Canadian horses with FFL, and 11 healthy stable mate controls. We found no significant difference in fecal microbial composition between FFL and healthy horses, which further supports that dysbiosis is not associated with FFL.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.747

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.183
GPT teacher head0.371
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2024
Admission routes3
Has abstractyes

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