Responses of <i>Megacyllene andesiana</i> and <i>Oreodera bituberculata</i> (Coleoptera: Cerambycidae) to <i>anti</i>-2,3-hexanediol, fuscumol, and fuscumol acetate in Peru
Bibliographic record
Abstract
Abstract Management of phytosanitary and biosecurity risks associated with the Cerambycidae focuses on prevention and early detection. Semiochemical-baited traps are an important component of these management efforts. Cerambycid pheromones are often screened in field trials to develop inventories of which species can be surveyed with which semiochemicals. We report field trials of two types of intercept traps (four- and three-sided panel traps) baited with known Cerambycidae pheromones aimed to capture Peruvian fauna. Intercept traps were baited with anti-2,3-hexanediol, fuscumol, and fuscumol acetate alone and in binary and ternary blends. The most frequently captured species was Megacyllene andesiana (Casey) (Coleoptera: Cerambycidae) (n = 268), followed by Oreodera bituberculata Bates (Coleoptera: Cerambycidae) (n = 59), Discopus eques Bates (Coleoptera: Cerambycidae) (n = 37), and Aegomorphus longitarsis (Bates) (Coleoptera: Cerambycidae) (n = 31). Trap type did not affect capture rates. Male and female M. andesiana were attracted by anti-2,3-hexanediol. The addition of fuscumol, fuscumol acetate, or the combination of fuscumol and fuscumol acetate reduced male M. andesiana captures, whereas the addition of fuscumol and the combination of fuscumol and fuscumol acetate reduced the response of female M. andesiana. Male O. bituberculata were attracted to traps baited with fuscumol, and this response was reduced by the addition of fuscumol acetate, whether or not anti-2,3-hexanediol was present.
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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".