Beef Cow-calf Vaccine Knowledge Translation and Transfer (KTT) Project: Summary report on producer, veterinarian, and working group surveys regarding vaccine usage and recommendations.
Bibliographic record
Abstract
Objective: The overall aim of this project was to create educational materials to support beef veterinarians and cow-calf producers in maximizing appropriate uptake of vaccine use in western Canada. The specific objective of the surveys reported here was to document current vaccine use by producers and vaccination recommendations by veterinarians and other industry stakeholders. Population: Cow-calf producers and veterinarians involved in the western Canadian beef cow-calf sector. Results: Surveys of western Canadian cow-calf producers and veterinarians were conducted in the fall of 2021 regarding current vaccine usage and recommendations, respectively. Uptake of beef cow-calf vaccines deemed "core" vaccines by the American Association of Bovine Practitioners (AABP) varied across cow-calf producers, and recommendations varied across veterinarians responding to the survey. Thirty members of the project working group, consisting of cow-calf producers, veterinarians, academics, and vaccine manufacturers, were also surveyed regarding vaccine recommendations. The recommendations of the working group aligned with AABP recommendations for core and risk-based vaccines. Conclusions: Uptake of core beef vaccines was not complete across the producers surveyed. Therefore, education of beef cow-calf producers regarding the importance of core vaccines is required. Clinical relevance: Findings from these surveys will guide creation of educational materials to promote the use of appropriate beef cow-calf vaccines.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".