Efficacy of unbaited and baited green multi-funnel traps for detection of Agrilus species and other wood-boring beetle taxa
Bibliographic record
Abstract
Abstract Semiochemical-baited traps are a key component of post-border surveillance for detection of non-native and potentially invasive bark and wood-boring beetles (Buprestidae, Cerambycidae, Curculionidae: Scolytinae) at risk of introduction in untreated woody materials used in global trade. Because the particular species that may arrive with imported goods is unknown, plant protection agencies need trapping protocols that effectively survey all three taxa. Baiting traps with host volatiles and aggregation/sex pheromones of longhorn beetles increases efficacy of detecting Cerambycidae and Scolytinae, but its effect on detection of Agrilus species and other jewel beetles is unknown. In this multi-country trapping study we found that the addition of ethanol and common aggregation/sex pheromones of longhorn beetles to green multi-funnel traps placed in the mid-upper forest canopy had negative effects on abundance of Agrilus species and other jewel beetles collected but no effect on their species richness, and significant positive effects on species richness and abundance of Cerambycidae and Scolytinae. Baiting green canopy traps with longhorn beetle pheromones increased the efficacy of traps for detecting total target taxa of bark and wood-boring beetles at risk of international movement in untreated woody materials. This information is beneficial for the design of multi-taxa surveys, potentially saving money and resources without decreasing trapping efficacy.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".