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Record W3025376746

Antimicrobial usage in western Canadian cow-calf herds.

2019· article· en· W3025376746 on OpenAlexaffabout
Cheryl Waldner, Sarah Parker, Sheryl Gow, Devon J. Wilson, John Campbell

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsFlorfenicolHerdOxytetracyclineAntimicrobialVeterinary medicineWeaningAnimal scienceBeef cattleBiologyAntibioticsMedicineMicrobiology
DOInot available

Abstract

fetched live from OpenAlex

While ongoing surveillance and research initiatives have provided some information on antimicrobial use (AMU) in many livestock commodities, there are no recent reports for Canadian cow-calf herds. Antimicrobial use data were collected in 2014 for bulls, cows, and calves from 100 herds participating in the Western Canadian Cow-Calf Surveillance Network. Lameness was the most common reason for treatment in cows and bulls, with oxytetracycline being the treatment of choice. Herd owners were most likely to treat calves before weaning with florfenicol, oxytetracycline, and sulfamethazine for respiratory disease or diarrhea. The most frequently reported reason for antimicrobial use in weaned calves was respiratory disease and the most reported product was florfenicol. While 98% of herds reported treating ≥ 1 animal with antimicrobials, most cattle did not receive antimicrobials for either treatment or disease prevention on participating cow-calf operations.

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 score0.019
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.228
Teacher spread0.212 · 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".

Quick stats

Citations33
Published2019
Admission routes2
Has abstractyes

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