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Record W3099035249 · doi:10.21423/aabppro20197208

Antimicrobial resistance in bovine respiratory disease

2019· article· en· W3099035249 on OpenAlexaff
Trent R Wennekamp, John Campbell, Cheryl Waldner, Kathy Larson, Claire Windeyer, A.I. Trokhymchuk

Bibliographic record

VenueAmerican Association of Bovine Practitioners Conference Proceedings · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsShared HealthUniversity of CalgaryUniversity of Saskatchewan
Fundersnot available
KeywordsBovine respiratory diseaseFeedlotFeeder cattleAntimicrobialAntibiotic resistanceDiseaseVeterinary medicineDominance (genetics)BiologyBiotechnologyMedicineAntibioticsMicrobiologyAnimal scienceInternal medicine

Abstract

fetched live from OpenAlex

Antimicrobials are important tools in modern beef production. Antimicrobial drugs are often used for metaphylaxis to prevent and treat early cases of bovine respiratory disease complex (BRD) as well as for therapeutic purposes in feedlot cattle. Antimicrobial resistance of bovine respiratory pathogens can result in treatment failures and losses associated with increased treatment costs and mortalities. Mixing of cattle from multiple sources within auction markets has always been a significant risk factor for BRD. The stress of establishing a social dominance hierarchy along with the mixing of pathogens from a variety of farm sources can increase the risk of BRD in weaned calves. One method feedlots have used to mitigate this risk is to reduce mixing stress by purchasing calves directly from the ranch rather than through the auction market. The objective of this study was to describe the prevalence and antimicrobial sensitivity of 3 major bovine respiratory disease (BRD) bacterial pathogens at arrival and again later in the feeding period in feedlot calves derived from the auction market and from a single ranch source.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.268
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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