Impact of Invasive Weevils on Agricultural Systems: Case Studies and Mitigation Strategies
Bibliographic record
Abstract
With the frequent international trade, the increase in seedling transportation and the intensification of global warming, the invasion and spread of weevils has become a major threat to global agricultural security. This study analyzes typical invasive weevils such as the red palm weevil (Rhynchophorus ferrugineus), the black-winged weevil (Myllocerus undatus) and the citrus root weevil (Diaprepes abbreviatus) to reveal the invasion pathways and spread mechanisms of weevils and their multidimensional impacts on agricultural ecosystems, including crop yield reduction, biodiversity decline and increased dependence on pesticides. In response to the challenges brought by invasive weevils, this study proposes comprehensive management strategies such as strengthening early monitoring and early warning systems, combining biological control with chemical control, and promoting the development of crop genetic resistance. In addition, it also emphasizes the need to strengthen international cooperation and quarantine system construction, and promote pest monitoring and ecological adaptability research based on big data and AI technology. The study believes that building an agricultural system framework with high ecological resilience and comprehensively using ecological, technological and management methods are the key ways to effectively prevent and control invasive weevils in the future. This study is not only conducive to reducing the economic losses caused by invasive weevils and improving food security, but also provides an important reference for achieving sustainable development of agricultural ecosystems, reflecting the strategic significance of the coordinated promotion of ecological protection and agricultural production safety.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".