Using blends of cerambycid beetle pheromones and host plant volatiles to simultaneously attract a diversity of cerambycid species
Bibliographic record
Abstract
We evaluated the attraction of native species of cerambycid beetles to blends of cerambycid pheromones and the host plant volatiles ethanol and α-pinene to determine whether such blends could be effective lures for detecting and monitoring multiple species. The complete six-component blend of pheromones included racemic 3-hydroxy-2-hexanone, 2,3-hexanediol isomers, (E)-6,10-dimethyl-5,9-undecadien-2-ol and the corresponding acetate, 2-(undecyloxy)-ethanol, and racemic 2-methyl-1-butanol. Bioassays in east-central Illinois captured 3070 cerambycid beetles of 10 species, including four species in the subfamily Cerambycinae ( Neoclytus acuminatus (Fabricius, 1775), Neoclytus mucronatus (Fabricius, 1775), Phymatodes lengi Joutel, 1911, and Xylotrechus colonus (Fabricius, 1775)) and six species in the subfamily Laminiae ( Aegomorphus modestus (Gyllenhal in Schoenherr, 1817), Astyleiopus variegatus (Haldeman, 1847), Astylidius parvus (LeConte, 1873), Graphisurus fasciatus (DeGeer, 1775), Lepturges angulatus (LeConte, 1852), and Monochamus carolinensis (Olivier, 1792)). Beetles were attracted to their pheromone components within the blend, with inhibition only evident in one species. Host plant volatiles synergized attraction for some species, and synergism usually was attributed to ethanol, with α-pinene enhancing attraction only for the pine specialist M. carolinensis. The optimal strategy for targeting a broad range of cerambycid species would be to bait traps with a blend of several pheromones plus ethanol and α-pinene because synergism by these plant volatiles is critical for some species, whereas strong inhibition is uncommon.
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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".