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Record W3025667606

Attitudes towards antimicrobial use and factors associated with antimicrobial use in western Canadian cow-calf herds.

2019· article· en· W3025667606 on OpenAlexaffabout
Cheryl Waldner, Sarah Parker, S. Gow, Wilson Dj, John Campbell

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsHerdAntimicrobialWeaningAnimal healthVeterinary medicineAnimal scienceMedicineAgricultural scienceBiologyMicrobiology
DOInot available

Abstract

fetched live from OpenAlex

One hundred cow-calf producers in western Canada were surveyed to determine their perceptions regarding antimicrobial use (AMU) and how these perceptions and other herd management factors were associated with AMU. Veterinarians were the most important source of AMU information. Half of the producers considered antimicrobial resistance (AMR) when choosing antimicrobials, while 24% considered the influence of AMU on AMR in human health. Younger producers < 30 y were most likely to consider AMR when choosing antimicrobials. Injectable products were used for disease prevention in 17% of herds; 5% used medically important antimicrobials in feed and 6% in water. Use of injectable antimicrobials of very high importance to human health was reported in 34% of herds. Producers with higher calf mortality were more likely to report AMU in feed or water. The use of Health Canada Category I antimicrobials was most common when calves were retained after weaning.

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)0.003
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 score0.121
Threshold uncertainty score0.243

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.092
GPT teacher head0.273
Teacher spread0.181 · 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

Citations13
Published2019
Admission routes2
Has abstractyes

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