New Pheromone Traps Lure Asian Longhorned Beetles Out of Hiding
Bibliographic record
Abstract
The Asian longhorned beetle (ALB) is one infamous insect these days. When it shows up, the media go crazy, wanted posters go up on busses and billboards, and many people get very, very worried. For this beetle is capable of killing healthy trees and has the potential to cause major ecosystem changes—if allowed to get out of control in the forests of North America. Maples, iconic trees for fall color in the Northeast, Midwest, and Canada, are its preferred larval host, but the ALB is known to develop in and destroy as many as 23 species of deciduous trees. In our cities, 35 percent of urban trees are at risk. ALB has no known natural enemies and the only registered pesticide treatment (soil drench or injection of imidacloprid) must be done by a registered pesticide applicator under supervision of the eradication program. The only effective way to kill the larvae is to chip infested material into tiny pieces. So, urban foresters, entomologists, natural resources managers, and homeowners ’ associations are all part of a campaign to eradicate the ALB in North America. Indeed, many who work professionally on the ALB problem call themselves “Beetlebusters”! Where did this beetle come from? The ALB was originally a very ordinary resident of the forests of China and Korea. But when susceptible poplars and willows were planted in fields and along canals, roads, and city streets in China, the beetles left their forest homes and started breeding in these plantations and
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".