An Overview of the Pepper Weevil (Coleoptera: Curculionidae) as a Pest of Greenhouse Peppers
Bibliographic record
Abstract
Abstract The pepper weevil (Anthonomus eugenii Cano) is a destructive insect pest of field and greenhouse pepper crops across North America. Its management remains challenging with significant implications for pepper production, despite a documented presence in Central America, Mexico, the United States, and the Caribbean for approximately a century, and recently in Canada. Currently, the main tools and methods applied to manage pepper weevil populations in greenhouse peppers are the implementation of strict biosecurity protocols, diligent monitoring, physical and cultural management techniques, and chemical insecticides when necessary. However, these tools can be costly, labor-intensive, and insufficient, particularly when new outbreaks go undetected for prolonged periods. Additionally, the use of available insecticides is limited due to significant nontarget effects these have on biological control agents used to manage other important greenhouse pepper pests. Recently, research efforts have focused on developing better tools for pepper weevil management to mitigate a rising incidence of insecticide resistance and the spread of weevils into temperate regions, however, multiple constraints remain. Here, we review the current state of knowledge of the pepper weevil and identify information gaps, which future research should address to improve the targeted management of this pest in greenhouse pepper production systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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".