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Comparative Microbiomes of the Respiratory Tract and Joints of Feedlot Cattle Mortalities

2022· article· en· W4206807867 on OpenAlexafffund
Chunli Li, Rahat Zaheer, Andrea Kinnear, Murray Jelinski, Tim A. McAllister

Bibliographic record

VenueMicroorganisms · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsUniversity of SaskatchewanAgriculture and Agri-Food Canada
FundersBeef Cattle Research Council
KeywordsBiologyFirmicutesBovine respiratory diseaseLungRespiratory tractMicrobiologyRespiratory systemActinobacteriaRespiratory diseaseBacteroidetesMicrobiomePathologyInternal medicineBacteriaMedicineAnatomy16S ribosomal RNA

Abstract

fetched live from OpenAlex

A comparative study of microbiota of the respiratory tract and joints of bovine respiratory disease (BRD) cattle mortalities was undertaken. Nasopharynx, trachea, lung and joint samples were collected from 32 cattle that died of BRD, “cases”, and 8 that died of other causes, “controls”. Bacterial diversity was lower (p < 0.05) in the nasopharynx, trachea and lungs of cases as compared to controls. In cases, alpha-diversity (p < 0.05) was lower in the lungs and joints than the nasopharynx. Proteobacteria, Tenericutes, Bacteroidetes, Firmicutes and Actinobacteria were the most abundant phyla in all samples. Relative abundances of Mycoplasma spp. in the lung, Pasteurella spp. in the trachea and lung, and Histophilus spp. in the lung, trachea and nasopharynx of cases were higher (p < 0.001) than controls. Mycoplasma spp. comprised 20.5% of bacterial flora in the joint, 36.0% in the lung, 22.4% in the trachea and 8.8% in the nasopharynx. Mannheimia spp. (21.8%) and Histophilus spp. (10.4%) were more abundant in lungs. Cattle that died of BRD possessed less diverse respiratory microbiomes with a higher abundance of respiratory pathogens. Mycoplasma spp. were prominent members of pneumonic lungs and joints displaying septic arthritis.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.280
Teacher spread0.247 · 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.

Study designBench or experimental
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

Citations11
Published2022
Admission routes2
Has abstractyes

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