Farmers who implemented this, also implemented that: Use of association-rule-learning to improve biosecurity on dairies
Bibliographic record
Abstract
Biosecurity practices are the cornerstone of disease prevention and control programs. In Canada, their implementation is evaluated with a Risk Assessment Questionnaire (RAQ). Association Rule Learning (ARL) – a non-supervised machine learning algorithm – is widely used in marketing for consumer segmentation based on purchase patterns. This technique may help veterinarians to recommend biosecurity practices that are more likely to be adopted by producers. In this project, we applied ARL to 3825 RAQ completed by Québec dairy producers to generate 22 million rules that identified combinations of self-reported practices frequently applied together. We retained the best 63 rules predicting the adoption of 13 biosecurity practices with a confidence ≥ 70 %. ARL is useful in studying the relationship between biosecurity practices on dairy farms. By identifying biosecurity practices more likely to be implemented by a given producer, veterinarians can provide targeted recommendations that might improve disease prevention and control programs. • Association Rule Learning (ARL) is a flexible technique to analyse questionnaires. • ARL predicted 13 key biosecurity practices more likely to be implemented by farmers. • Implemented practices were predicted with a confidence higher than 70 %. • ARL allows prescriptive use of questionnaires and personalized recommendations. • ARL may enhance uptake of disease prevention and control programs.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".