Interactions between Ethanol, syn-2,3-Hexanediol, 3-Hydroxyhexan-2-one, and 3-Hydroxyoctan-2-one Lures on Trap Catches of Hardwood Longhorn Beetles in Southeastern United States
Bibliographic record
Abstract
The effectiveness of a four-component "super lure" consisting of ethanol (E) and the cerambycid pheromones syn-2,3-hexanediol (D6), racemic 3-hydroxyhexan-2-one (K6), and racemic 3-hydroxyoctan-2-one (K8) on trap catches of Cerambycidae (Coleoptera) was determined in southeast United States with seven trapping experiments in 2011-2013. We captured 74 species of longhorn beetles in our three-year study. Ethanol significantly increased the mean catches of seven species and increased the number of cerambycid species detected. Traps with the "super lure" were effective for 8 of 13 species of Cerambycidae previously shown to be attracted to binary combinations of ethanol plus one of the three pheromones. However, the "super lure" was less effective for the remaining five species with catch reductions of 40-90% compared with combinations of ethanol and one or two of the pheromones. For example, K6 + K8 lures reduced catches of Anelaphus villosus (F.) in traps with E + D6 by 90%. Similarly, catches of Anelaphus pumilus (Newman) in traps with E + K6 + D6 were reduced by 50% with the addition of K8. Catches of Knulliana cincta (Drury) in traps with K6 + K8 lures were interrupted by D6, an effect negated by the addition of ethanol. Given the interruptive effects on trap catches of some species when lures are combined in a single trap, developing optimal lure blends to maximize detection efficacy will be a challenge for managers of detection programs for non-native invasive species of longhorn beetles.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".