Agent secret de lutte biologique! Amélioration de la lutte contre la teigne du poireau en Ontario et au Québec
Bibliographic record
Abstract
In 2023, AAFC's Pest Management Centre (PMC) celebrates its 20 th anniversary.This story is one of many examples of projects funded through PMC to help growers manage pest problems such as weeds, insects, diseases, and nematodes which can threaten the quality, value, and yield of the crops they produce.The leek moth, an invasive pest from Europe, causes extensive damage and economic losses in allium crops such as onions, leeks, and garlic.Without control, moth populations reaching the third generation, within a growing season, can destroy 100% of these crops.Since first detection in 1993, by an Agriculture and Agri-Food Canada (AAFC) taxonomist, scientists have been on the casebuilding monitoring techniques and a development model to predict the occurrence of different stages of the moth's lifecycle, as well as investigating various pest control options including a parasitic wasp, Diadromus pulchellus (released as a self-sustaining biological control in 2010).The leek moth has rapidly expanded from eastern Ontario to southwestern Ontario, Quebec, Prince Edward Island, Nova Scotia, and the US -a trend that will accelerate as our climate warms.Scientists also predict an increase in the number of generations of this pest each growing season.That is why AAFC's Pest Management Centre (PMC) has facilitated two decades of research to create a multi-pronged, integrated package, to help growers sustainably manage this devastating pest.Projet d'AAC PRR10-030 : Dissémination d'une guêpe parasitoïde comme moyen de lutte biologique contre la teigne du poireau au Canada, https://agriculture.canada.ca/fr/science/centres-recherche-lagriculture-lagroalimentaire/cen- tre-lutte-antiparasitaire/reduction-risques-lies-aux-pesticides-au-centre-lutte-antiparasitaire/projets-lutte-integree/dissemination-guepe-parasitoide-moyen-lutte-biologique-contre-teigne-du
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.112 | 0.012 |
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".