Efficacy of trapping protocols for Agrilus jewel beetles: a multi-country assessment
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The genus Agrilus is one of the most diverse insect genera worldwide. The larval feeding activity causes extensive damage in both forests and orchards. In addition, more than 30 species have been introduced outside their native range so far, including the emerald ash borer Agrilus planipennis Fairmaire. Thus, the availability of efficient trapping protocols for early detection of Agrilus species at entry points is of utmost importance. In this study we tested whether trapping protocols developed for surveillance of A. planipennis in North America were also effective for other Agrilus species. In particular, through a multi-country assessment we compared the efficacy of detecting Agrilus species on: (i) green glue-coated prism traps vs. green Fluon-coated multi-funnel traps when baited with the green leaf volatile ( Z )-3-hexenol or left unbaited; and (ii) green multi-panel traps vs. green multi-panel traps baited with dead adult Agrilus beetles (decoys). A total of 23,481 individuals from 45 Agrilus species were caught. Trap design significantly affected both species richness and abundance of Agrilus species in several of the countries where the trapping experiments were carried out, and green prism traps outperformed green multi-funnel traps in most cases. On the contrary, the addition of a ( Z )-3-hexenol lure or dead adult beetle decoys on to traps did not improve trap catches. Our study highlights that reliable trap models to survey Agrilus species are already available, but also that there is the clear need to further investigate chemical ecology of Agrilus species to develop semiochemical lures that can improve detection 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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