Flea beetle (<i>Phyllotreta</i> spp.) management in spring‐planted canola (<i>Brassica napus</i> L.) on the northern Great Plains of North America
Bibliographic record
Abstract
Abstract Canola (Brassica napus L. and B. rapa L. [Brassicales: Brassicaceae]) is a major oilseed crop grown globally as a source of vegetable oil, animal feed and biofuel feedstock. The global demand for canola oil as a biofuel feedstock has increased due to recent regulations in the European Union, United States, and Canada. In North America, canola production is centered on the northern Great Plains where it is challenged by two highly destructive flea beetle species, the crucifer (Phyllotreta cruciferae Goeze, 1777) and the striped (Phyllotreta striolata Fabricius, 1803) flea beetles. In the spring, adult P. cruciferae and P. striolata begin feeding on canola seedlings, creating a ‘shot hole’ appearance, which can reduce the plant's photosynthetic capacity leading to uneven plant emergence and growth, reduced plant stand density, and reduced seed yield. Losses resulting from flea beetles are estimated in the tens of millions of dollars annually. At present, the principle means for flea beetle control are insecticides applied as systemic seed treatments and/or subsequent foliar sprays. The continued use of these products is being questioned due to environmental concerns and acquisition of resistance. As such, significant research effort is being directed toward the development of an integrated pest management system for these abundant and hard to manage pests of canola. Here, we review the ecology, pest status, and management of flea beetles in North America and discuss future research needed to promote flea beetle management and sustainable canola production.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".