Differential Bio-Activity of<i>Ips</i>and<i>Dendroctonus</i>(Coleoptera: Scolytidae) Pheromone Components for<i>Monochamus clamator</i>and<i>M. scutellatus</i>(Coleoptera: Cerambycidae)
Bibliographic record
Abstract
Male and female Monochamus clamator (LeConte) and M. scutellatus (Say) are able to detect bark beetle pheromone components electrophysiologically and are attracted to traps baited with blends of pheromone components of scolytid bark beetles. We investigated the effect of individual pheromone components (ipsenol, ipsdienol, 3-methyl-2-cyclohexen-1-one (MCH), frontalin, verbenone, cis- and trans-verbenol, and endo- and exo-brevicomin) on Monochamus Dejean trap catches. Only traps baited with ipsenol and/or ipsdienol together with the host volatiles ethanol and α-pinene caught significantly more male and female M. scutellatus and M. clamator than traps baited with host volatiles alone. Ipsenol and ipsdienol are aggregation pheromones of secondary bark beetles in the genus Ips DeGeer while the other components are pheromones of primary bark beetles in the genus Dendroctonus Erichson. The former should be the most reliable indicators of suitable host material because most Ips spp. attack weakened or moribund trees or trees already successfully under attack by primary bark beetles, and their pheromones may be more persistent in space and time than those of Dendroctonus spp. In two successive years in an operational (commercial) mass-trapping program, traps baited with ethanol, -pinene, and ipsenol captured twice as many beetles as traps baited with host volatiles alone. These results suggest that operational monitoring or mass-trapping programs could be improved significantly by the inclusion of ipsenol in baits at a minimal cost.
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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".