Cerambycid Pheromones 3,2-Hydroxyketones Affect Catches of Some Bark and Ambrosia Beetles (Coleoptera: Curculionidae) in Ethanol-Baited Multiple-Funnel Traps in Southeastern United States
Bibliographic record
Abstract
In 2012-2013, we assessed the interactive effects of the cerambycid pheromones syn-2,3-hexanediol, 3-hydroxyhexan-2-one, and 3-hydroxyoctan-2-one on catches of bark and ambrosia beetles (Coleoptera: Curculionidae) in ethanol-baited multiple-funnel traps in north Georgia and South Carolina. We found that catches for nine of eleven species of ambrosia beetles in ethanol-baited traps were either unaffected or enhanced by the addition of 3,2-hydroxyketones. Similarly catches of five species of bark beetles were either unaffected or enhanced by the addition of 3,2-hydroxyketones. In particular, catches of Xylosandrus crassiusculus (Motschulsky), Cnestus mutilatus (Blandford), and Monarthrum fasciatum (Say) in ethanol-baited traps increased with the addition of 3-hydroxyhexan-2-one and/or 3-hydroxyoctan-2-one. Catches of the bark beetles Hylocurus rudis (LeConte) and Hypothenemus rotundicollis (Eichhoff) were enhanced by the addition of 3-hydroxyhexan-2-one and 3-hydroxyoctan-2-one, respectively. syn-2,3-Hexanediol had no effect on catches of bark and ambrosia beetles in ethanol-baited traps. Our data provide support for the use of ethanol + cerambycid pheromones for targeting non-native species of bark and ambrosia beetles as well as cerambycids in detection programs.
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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 | 0.000 | 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 | 0.000 |
| 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".