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Record W3108030016 · doi:10.21423/aabppro20197212

Comparison of the nasopharyngeal bacterial microbiota of beef calves raised without the use of antimicrobials between healthy calves and those diagnosed with bovine respiratory disease

2019· article· en· W3108030016 on OpenAlexaff
C. M. McMullin, Karin Orsel, Trevor W. Alexander, F. J. Van der Meer, Graham Plastow, Edouard Timsit

Bibliographic record

VenueAmerican Association of Bovine Practitioners Conference Proceedings · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsBovine respiratory diseaseFeedlotAntimicrobialBeef cattleBiologyMicrobiological cultureDiseaseMicrobiologyMedicineImmunologyBacteriaAnimal scienceInternal medicine

Abstract

fetched live from OpenAlex

It has been shown that the composition of the nasopharyngeal bacterial microbiota plays a role in respiratory health. However, the role of this microbiota in the development of bovine respiratory disease (BRD) is still not well defined. What we know about the composition of the bovine nasopharyngeal bacterial microbiota mainly comes from beef calves that have received mass medication (metaphylaxis) with an antimicrobial on arrival at a feedlot. Unfortunately, antimicrobials do not just target pathogenic bacteria, and antimicrobial use can have an impact on the entire respiratory microbiota, including commensals. There are currently no data on the composition of the nasopharyngeal microbiota of healthy feedlot cattle and those diagnosed with BRD that are raised without antimicrobials (i.e. natural cattle), limiting our understanding of the disease. As the beef industry begins to move away from the use of antimicrobials, this information will become increasingly valuable. Therefore, the objective of this study was to characterize and compare the nasopharyngeal bacterial microbiota in feedlot cattle raised without antimicrobials that were healthy or diagnosed with BRD.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.302
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.043
GPT teacher head0.322
Teacher spread0.279 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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