Pine Sawyers (Coleoptera: Cerambycidae) Attracted to α-Pinene, Monochamol, and Ipsenol in North America
Bibliographic record
Abstract
Detection tools are needed for Monochamus species (Coleoptera: Cerambycidae) because they are known to introduce pine wilt disease by vectoring nematodes in Asia, Europe, and North America. In 2012-2014, we examined the effects of the semiochemicals monochamol and ipsenol on the flight responses of the sawyer beetles Monochamus carolinensis (Olivier), Monochamus clamator (LeConte), Monochamus mutator LeConte, Monochamus notatus (Drury), Monochamus obtusus Casey, Monochamus scutellatus (Say), and Monochamus titillator (F.) complex (Coleoptera: Cerambycidae) to traps baited with α-pinene. Experiments were set in pine forests in New Brunswick and Ontario (Canada), and Arizona, Georgia, Michigan, Montana, Oregon, South Carolina, Utah, and Washington (United States). In brief, 40 traps were placed in 10 blocks of 4 traps per block per location. Traps were baited with: 1) α-pinene; 2) α-pinene + monochamol; 3) α-pinene + ipsenol; and 4) α-pinene + monochamol + ipsenol. Monochamol increased catches of six species and one species complex of Monochamus with an additive effect of ipsenol for five species and one species complex. There was no evidence of synergy between monochamol and ipsenol on beetle catches. Monochamol had no effect on catches of other Cerambycidae or on any associated species of bark beetles, weevils, or bark beetle predators. We present a robust data set suggesting that the combination of α-pinene, ipsenol, and monochamol may be a useful lure for detecting Monochamus species.
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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".