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Record W3134308167 · doi:10.21423/aabppro20134218

Identification and determination of the antimicrobial susceptibility of the main respiratory pathogens isolated from calves in dairy herds with respiratory diseases in Quebec

2013· article· en· W3134308167 on OpenAlexaffabout
David Francoz, Sébastien Buczinski, Olivia Labrecque, A.M. Bélanger, Vincent Wellemans, J. Dubuc

Bibliographic record

VenueAmerican Association of Bovine Practitioners Conference Proceedings · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsMinistère de l'Agriculture, des Pêcheries et de l'AlimentationUniversité de Montréal
Fundersnot available
KeywordsBovine respiratory diseaseCullingEnzooticBiologyDairy cattleHerdBovine coronavirusPneumoniaVeterinary medicineVirologyMicrobiologyMedicineVirusAnimal scienceDiseaseInternal medicineCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Bovine respiratory disease (BRD) represents 1 of the 2 most important causes of morbidity and death in dairy calves. The detrimental effects of BRD in calves on dairy herds can be substantial and includes decreased profitability because of cost of treatments, decreased growth rate, increased risk of culling before first calving, and death. Calf enzootic pneumonia is associated with multiple bacterial and viral pathogens. In the past 10 years, emerging and re-emerging respiratory pathogens, such as bovine respiratory syncytial virus (BRSV), bovine coronavirus (BCV), and Mycoplasma bovis, have been identified as playing a major role in the development of BRD in dairy calves in North America. Field data regarding the prevalence of various BRD pathogens in dairy calves, particularly calves on small farms, are lacking. The objectives of this study were to identify the primary respiratory pathogens isolated from calves with BRD in Quebec dairy herds and to determine the antimicrobial susceptibility of those pathogens.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.248
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

Citations0
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueAmerican Association of Bovine Practitioners Conference ProceedingsSame topicMicrobial infections and disease researchFrench-language works237,207