Evaluating the Role of Seed Treatments in Canola/Oilseed Rape Production: Integrated Pest Management, Pollinator Health, and Biodiversity
Bibliographic record
Abstract
The use patterns and role of insecticide seed treatments, with focus on neonicotinoid insecticides, were examined for canola/oilseed rape production in Canada and the EU. Since nearly all planted canola acres in Western Canada and, historically, a majority of planted oilseed acres in the EU, use seed treatments, it is worth examining whether broad use of insecticidal seed treatments (IST) is compatible with principles of integrated pest management (IPM). The neonicotinoid insecticide (NNI) seed treatment (NNI ST) use pattern has risen due to effective control of several early season insect pests, the most destructive being flea beetles (Phyllotreta sp.). Negative environmental impact and poor efficacy of foliar applied insecticides on flea beetles led growers to look for better alternatives. Due to their biology, predictive models have been difficult to develop for flea beetles, and, therefore, targeted application of seed treatments, as part of an IPM program, has contributed to grower profitability and overall pollinator success for canola production in Western Canada. Early evidence suggests that the recent restriction on NNI may negatively impact grower profitability and does not appear to be having positive impact on pollinator health. Further investigation on impact of NNI on individual bee vs. hive health need to be conducted. Predictive models for flea beetle emergence/feeding activity in canola/oilseed rape need to be developed, as broad acre deployment of NNI seed treatments may not be sustainable due to concerns about resistance/tolerance in flea beetles and other pest species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".