Biology, Ecology, and Management of Flea Beetles in <i>Brassica</i> Crops
Bibliographic record
Abstract
Brassica vegetable and oilseed crops are attacked by several different flea beetle species (Chrysomelidae: Alticini). Over the past decades, most research has focused on two Phyllotreta species, Phyllotreta striolata and Phyllotreta cruciferae, which are major pests of oilseed rape in North America. More recently, and especially after the ban of neonicotinoids in the European Union, the cabbage stem flea beetle, Psylliodes chrysocephala, has become greatly important and is now considered to be the major pest of winter oilseed rape in Europe. The major challenges to flea beetle control are the prediction of population dynamics in the field, differential susceptibility to insecticides, and the lack of resistant plant cultivars and other economically viable alternative management strategies. At the same time, many fundamental aspects of flea beetle biology and ecology, which may be relevant for the development of sustainable control strategies, are not well understood. This review focuses on the interactions between flea beetles and plants and summarizes the literature on current management strategies with an emphasis on the potential for biological control in flea beetle management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".