Evaluation des mesures de biosécurité dans les fermes avicoles au Québec par vidéosurveillance et principales erreurs commises
Bibliographic record
Abstract
Evaluation of biosecurity measures based on video surveillance in poultry farms in Quebec and main failures. Biosecurity measures are designed to prevent the introduction of infectious diseases in flocks, and reduce the consequences of an infection. However, to be effective, biosecurity measures must be applied consistently by all. Poor compliance has been reported with all types of animal production, and many reasons have been given, such as the lack of understanding of biosecurity principles. It is essential to define strategies to improve the implementation of biosecurity measures. Different approaches have been studied in human medicine, mainly in hospital settings. They include daily observations and feedback to employees, training programs, the presence of an observer, and the increased availability of hand washing stations. These strategies have been shown to work, but only for the short term. We are currently conducting a study in 24 poultry farms in Quebec to determine the impact of audits and of visible cameras on the level of biosecurity compliance. The effect of these two strategies will be determined in the short term (2 weeks) and in the medium term (six months later). The targeted biosecurity measures are those required when getting in and out of poultry barns. The compliance is evaluated using hidden cameras. People filmed during the study will then be asked to complete a questionnaire designed to assess their personality profile. The first objective of the study is to determine whether audits or visible cameras increase biosecurity compliance. The second objective is to determine whether a relationship exists between personality profiles and compliance. Preliminary results from 13 farms show that poor compliance is indeed a present-day problem.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".