Graduate Student Literature Review: Perceptions of biosecurity in a Canadian dairy context
Bibliographic record
Abstract
The objective of this review was to outline current implementation of biosecurity, the impact of biosecurity on the industry, and producers' and veterinarians' perceptions of biosecurity, with a focus on the Canadian dairy industry. Biosecurity has an important role in farm safety by reducing the spread of pathogens and contaminants, improving animal health and production, and maintaining human safety. Implementation of biosecurity practices varies among farms and countries. Because Canada's supply management system is different than other countries, different barriers and perceptions of biosecurity may exist. Producers may have negative perspectives on biosecurity, such as it being expensive or time consuming. Producers are motivated or deterred from biosecurity implementation for many reasons, including perceived value, disease risk, and financial incentives or deterrents. In addition, because veterinarians are a trusted source of information, their approaches to discussions on biosecurity implementation are important to understand. Veterinarians and producers appear to have differing opinions on the importance of biosecurity and approaches to discussing biosecurity. Improving biosecurity implementation requires a multifactorial approach, such as individualized education and awareness for producers, further research into efficacy of and barriers to biosecurity, and development of strategies for effective communication between veterinarians and producers.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".