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Record W3133685027 · doi:10.1017/s1466252320000316

The role of the bovine respiratory bacterial microbiota in health and disease

2020· article· en· W3133685027 on OpenAlexaff
Trevor W. Alexander, Edouard Timsit, Samat Amat

Bibliographic record

VenueAnimal Health Research Reviews · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsBovine respiratory diseaseFeedlotBiologyAntibioticsMicrobiologyRespiratory tractWeaningAntibiotic resistanceRespiratory systemAntimicrobialAnimal science

Abstract

fetched live from OpenAlex

Increased antimicrobial resistance in bovine respiratory bacterial pathogens poses a threat to the effective control and prevention of bovine respiratory disease (BRD). As part of continued efforts to develop antimicrobial alternatives to mitigate BRD, the microbial community residing within the respiratory tract of feedlot cattle has been increasingly studied using next-generation sequencing technologies. The mucosal surfaces of upper and lower respiratory tracts of cattle are colonized by a diverse and dynamic microbiota encompassing commensal, symbiotic, and pathogenic bacteria. While a direct causal relationship between respiratory microbiota and the development of BRD in feedlot cattle has not been fully elucidated, increasing evidence suggests that the microbiota contributes to respiratory health by providing colonization resistance against pathogens and maintaining homeostasis. Certain management practices such as weaning, transportation, feed transition, and antibiotic application can disrupt the respiratory microbiota, potentially altering pathogen colonization. Microbiota-based approaches, including bacterial therapeutics that target restoring the normal respiratory microbiota, may provide new methods for mitigating BRD in feedlot cattle in place of antibiotics. In addition, the distinct bacterial respiratory microbial communities observed in BRD-affected and healthy feedlot cattle may allow for future application of microbiota-based techniques used in the diagnosis of 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)0.005
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.763
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.154
GPT teacher head0.421
Teacher spread0.266 · 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
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

Citations22
Published2020
Admission routes1
Has abstractyes

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