Organic management of the invasive swede midge (Diptera: Cecidomyiidae) on <i>Brassica</i> vegetables: multiple dead ends necessitate novel approaches
Bibliographic record
Abstract
Abstract The distinctive biology and ecology of the invasive swede midge, Contarinia nasturtii Kieffer (Diptera: Cecidomyiidae), render organic management in North America particularly challenging, necessitating the search for novel out-of-the box approaches to control it. Native to Eurasia, this pest was first confirmed in North America in 2000 and has since spread to several states and provinces in the United States and Canada. As a galling midge, swede midge feeding causes distorted growth in Brassica (Brassicales: Brassicaceae) vegetables, resulting in major losses of marketable produce. While conventional growers typically use systemic insecticides for plant protection early in the season, equivalent approaches for organic production do not exist. The lack of effective organic management approaches for swede midge has resulted in devastating losses for heading Brassica vegetables, which are the most sensitive to midge feeding. Here, we review over 2 decades of research focused on developing organic approaches to manage swede midge. To encourage more rapid progress on swede midge management, we believe that it is important to review the widest array of work, including recent publications as well as our unpublished research. We conclude by highlighting the most promising strategies that should be utilized on farms and explored further for organic management of swede midge in vegetable crops.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".