Efficacy of unbaited and baited green multi-funnel traps for detection of Agrilus species and other wood-boring beetle taxa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it