Using a biovigilance approach for pest and disease management in Quebec vineyards
Bibliographic record
Abstract
Biovigilance can be defined as the study of the unintentional effects of various factors on pest (insects, pathogens, and weeds) populations, biodiversity and ecological services. Biovigilance research allows for the detection of significant temporal and spatial trends that may be linked to agricultural practices, new plant protection products, cultivars grown, new crops, climate change, or emerging and new pests. Since the 1950s, farming and pest management practices have undergone considerable changes driven notably by increased mechanization, intensive use of fertilizers and pesticides, and genetic improvement of crops. These practices have exerted pressure on pest populations, which have adapted by, among other means, developing pesticide resistance and overcoming crop resistance. In addition, climate change and the movement of plant products between different areas or countries also influence the diversity of pest populations and their natural enemies. As a result, pest management decisions must take into account these changes. Biovigilance-based information can be used for strategic (long-term) decisions about matters such as the type of production system, crop rotation, and host genetic selection for perennial crops. Similarly, knowledge on pest aggressiveness or pesticide resistance should be considered when making tactical (short-term) disease management decisions. Ultimately, biovigilance provides frameworks to address the increasing complexity of plant protection. The ultimate objective of biovigilance-based pest management is to mitigate potential threats before they become important problems. Typically, this approach to pest management is forward-looking and requires a long-term commitment for research. Viticulture in eastern Canada illustrates how biovigilance information can help to make optimal pest management decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".