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Record W2884902542 · doi:10.3390/microorganisms6030076

A Comparative Study on the Faecal Bacterial Community and Potential Zoonotic Bacteria of Muskoxen (Ovibos moschatus) in Northeast Greenland, Northwest Greenland and Norway

2018· article· en· W2884902542 on OpenAlexaboutno aff
Emilie Andersen-Ranberg, Christopher J. Barnes, L. Rasmussen, A. Flores, Carsten Grøndahl, Jesper Bruun Mosbacher, Anders J. Hansen, Monica A. Sundset, Niels Martin Schmidt, Christian Sonne

Bibliographic record

VenueMicroorganisms · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsnot available
FundersNordisk Ministerråd
KeywordsArcticBiologyGroenlandiaEcologyZoologyIce sheet

Abstract

fetched live from OpenAlex

Muskoxen (Ovibos moschatus) are ruminants adapted to a high-fibre diet. There is increasing interest in the role that gut microbes play in the digestion and utilization of these specialized diets but only limited data available on the gut microbiome of high-Arctic animals. In this study, we metabarcoded the 16S rRNA region of faecal samples from muskoxen of Northeast Greenland, Northwest Greenland and Norway, and quantified the effects of physiological and temporal factors on bacterial composition. We found significant effects of body mass, year of sampling and location on the gut bacterial communities of North East Greenland muskoxen. These effects were however dwarfed by the effects of location, emphasizing the importance of the local ecology on the gut bacterial community. Habitat alterations and rising temperatures may therefore have a considerable impact on muskoxen health and reproductive success. Moreover, muskoxen are hunted and consumed in Greenland, Canada and Alaska; therefore, this study also screened for potential zoonoses of food safety interest. A total of 13 potentially zoonotic genera were identified, including the genera Erysipelothrix and Yersinia implicated in recent mass die-offs of the muskoxen themselves.

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 score0.814
Threshold uncertainty score0.951

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 head0.017
GPT teacher head0.256
Teacher spread0.239 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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