Biosecurity adoption in Québec dairy farms: Results from a risk assessment questionnaire analyzed using conventional and unsupervised artificial intelligence methods
Bibliographic record
Abstract
This study documents the current state of biosecurity on dairy farms in Québec following the implementation of a mandatory biosecurity risk evaluation that was part of the proAction accreditation program developed by Dairy Farmers of Canada. Using a cross-sectional design, 3,825 risk assessment questionnaires completed between 2018 and 2021 were extracted from Vigil-Vet database, which is a software used by veterinarians for conducting the proAction risk assessment. Descriptive statistics were used to summarize the practices adopted by dairy producers. Additionally, multiple correspondence analysis was used to explore the association between the diseases of most concern and the adoption of biosecurity practices. Moreover, we used a hierarchical cluster analysis on principal components to identify distinct patterns of biosecurity practices among dairy producers. This analysis enabled the identification of typologies or clusters of farms based on the specific biosecurity practices they currently employ. The results of the descriptive statistics indicated that mastitis was the disease of most concern for most dairy farmers (40%). Moreover, given that only 10% of the 2,237 dairy farmers who acquired animals adhered to quarantine practices, there seems to be a need for improved implementation of biosecurity measures aimed at restricting the introduction of diseases when introducing new animals. Conversely, cleaning stalls and health equipment were adequately addressed by 95% and 86% of dairy producers, respectively. The multiple correspondence analysis indicated no significant association between the disease of most concern and the farm's biosecurity profile, except for respondents who identified digital dermatitis as their disease of most concern. Through the hierarchical cluster analysis, 3 clusters were identified among 3,581 farms: (1) Cluster 1 included farms with good management of sick animals; (2) Cluster 2 included farms with good management of young animals; and (3) Cluster 3 included farms with poor management of sick animals and young animals. Our study makes an important contribution by providing valuable insights into the biosecurity practices currently adopted on Québec dairy farms. It establishes a baseline for assessing progress in biosecurity practices adoption and serves as a reference point for future evaluations. In addition, these findings play a key role in monitoring the effectiveness of interventions aimed at improving biosecurity on dairy farms. By making use of this knowledge, stakeholders can make informed decisions that prioritize animal health, increase productivity, and ensure sustainability of the dairy industry.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".