Current and future control of the wood‐boring pest <i>Anoplophora glabripennis</i>
Bibliographic record
Abstract
The Asian longhorn beetle (ALB) Anoplophora glabripennis is one of the most successful and most feared invasive insect species worldwide. This review covers recent research concerning the distribution of and damage caused by ALB, as well as major efforts to control and manage ALB in China. The distribution and destruction range of ALB have continued to expand over the past decade worldwide, and the number of interceptions has remained high. Detection and monitoring methods for the early discovery of ALB have diversified, with advances in semiochemical research and using satellite remote sensing in China. Ecological control of ALB in China involves planting mixtures of preferred and resistant tree species, and this practice can prevent outbreaks. In addition, strategies for chemical and biological control of ALB have achieved promising results during the last decade in China, especially the development of insecticides targeting different stages of ALB, and applying Dastarcus helophoroides and Dendrocopos major as biocontrol agents. Finally, we analyze recommendations for ALB prevention and management strategies based on native range and invasive area research. This information will hopefully help some invaded areas where the target is containment of ALB.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".