Survey to describe beef producer opinions on antibiotic use and consumer perceptions of antibiotics in the beef industry
Bibliographic record
Abstract
Beef producers and veterinarians work together to improve cattle health and well-being. Antibiotic use and resistance is of interest to all involved in the beef industry, including beef consumers. A 26-question survey was developed by veterinarians at Kansas State University to explore antibiotic use and opinions on contemporary issues at the beef producer level. The survey was advertised throughout the United States and Canada via popular industry outlets. A total of 260 surveys were completed by beef producers from 43 states and 1 province in Canada. Beef producers operating cow-calf operations represented 88% of the respondents. Producers managing stocker, backgrounder, and feeder operations were represented in equal proportions in the remaining survey responses. Eighty-five percent (85%) of beef producers indicated they use the services of a veterinarian regularly, while only 23% reported that they have a written, documented, and signed veterinary-client-patient relationship. Participants indicated that they rarely use oral and injectable antibiotics. The most common indication for antibiotic use in cattle managed by respondents was for the treatment of bovine respiratory disease, foot rot, and pinkeye. Seventy-two percent (72%) of producers indicated that Beef Quality Assurance is an important industry program for addressing antibiotic use and prevention of antibiotic residues. When asked if familiar with the Veterinary Feed Directive rule, 81% of respondents indicated they had knowledge of the law. These data illustrate that beef producers are willing to share information about their production systems and management strategies, including information on antibiotic use in cattle.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".