<i>Drosophila suzukii</i> (Diptera: Drosophilidae): A Decade of Research Towards a Sustainable Integrated Pest Management Program
Bibliographic record
Abstract
Drosophila suzukii (Matsumura) (Diptera: Drosophilidae) also known as spotted-wing drosophila (SWD), is a pest native to Southeast Asia. In the last few decades, the pest has expanded its range to affect all major European and American fruit production regions. SWD is a highly adaptive insect that is able to disperse, survive, and flourish under a range of environmental conditions. Infestation by SWD generates both direct and indirect economic impacts through yield losses, shorter shelf life of infested fruit, and increased production costs. Fresh markets, frozen berries, and fruit export programs have been impacted by the pest due to zero tolerance for fruit infestation. As SWD control programs rely heavily on insecticides, exceedance of maximum residue levels (MRLs) has also resulted in crop rejections. The economic impact of SWD has been particularly severe for organic operations, mainly due to the limited availability of effective insecticides. Integrated pest management (IPM) of SWD could significantly reduce chemical inputs but would require substantial changes to horticultural management practices. This review evaluates the most promising methods studied as part of an IPM strategy against SWD across the world. For each of the considered techniques, the effectiveness, impact, sustainability, and stage of development are discussed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".