Ipsenol, Ipsdienol, Ethanol, and α-Pinene: Trap Lure Blend for Cerambycidae and Buprestidae (Coleoptera) in Pine Forests of Eastern North America
Bibliographic record
Abstract
In 2007-2008, we examined the flight responses of wood-boring beetles (Coleoptera: Cerambycidae and Buprestidae) to multiple-funnel traps baited with the pine volatiles, ethanol, and α-pinene [85% (-)], and the bark beetle pheromones, racemic ipsenol and racemic ipsdienol. Experiments were conducted in mature pine stands in Canada (Ontario and New Brunswick) and the United States (Arkansas, Florida, Michigan, New Hampshire, North Carolina, Ohio, Tennessee, and Wisconsin). At each location, traps were deployed in 10 replicate blocks of four traps per block. The trap treatments were: 1) blank control; 2) ipsenol and ipsdienol; 3) ethanol and α-pinene; and 4) a quaternary blend of ipsenol, ipsdienol, ethanol, and α-pinene. Traps baited with the quaternary blend caught the greatest numbers of Acanthocinus nodosus (F.), Acanthocinus obsoletus (Olivier), Acmaeops proteus (Kirby), Astylopsis sexguttata (Say), Rhagium inquisitor (L.) (Cerambycidae), and Buprestis lineata (F.) (Buprestidae). Traps baited with ethanol and α-pinene caught the greatest numbers of Arhopalus rusticus (LeConte), Asemum striatum (L.), Tetropium spp., Xylotrechus sagittatus (Germar) (Cerambycidae), and Buprestis maculipennis Gory (Buprestidae) with minimal interruption by ipsenol and ipsdienol. Our results suggest that multiple-funnel traps baited with the quaternary lure blend of ipsenol, ipsdienol, ethanol, and α-pinene are effective for trapping various species of wood-boring beetles in pine forests of eastern North America, and may have utility in detection programs for adventive species in North America and overseas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".