Diamondback Moth Ecology and Management: Problems, Progress, and Prospects
Bibliographic record
Abstract
Agricultural intensification and greater production of Brassica vegetable and oilseed crops over the past two decades have increased the pest status of the diamondback moth (DBM), Plutella xylostella L., and it is now estimated to cost the world economy US$4-5 billion annually. Our understanding of some fundamental aspects of DBM biology and ecology, particularly host plant relationships, tritrophic interactions, and migration, has improved considerably but knowledge of other aspects, e.g., its global distribution and relative abundance, remains surprisingly limited. Biological control still focuses almost exclusively on a few species of hymenopteran parasitoids. Although these can be remarkably effective, insecticides continue to form the basis of management; their inappropriate use disrupts parasitoids and has resulted in field resistance to all available products. Improved ecological understanding and the availability of a series of highly effective selective insecticides throughout the 1990s provided the basis for sustainable and economically viable integrated pest management (IPM) approaches. However, repeated reversion to scheduled insecticide applications has resulted in resistance to these and more recently introduced compounds and the breakdown of IPM programs. Proven technologies for the sustainable management of DBM currently exist, but overcoming the barriers to their sustained adoption remains an enormous challenge.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".