Screening known Cerambycidae pheromones for activity with the Peruvian fauna
Bibliographic record
Abstract
Abstract Semiochemicals are powerful tools for the surveillance and suppression of forest insects. Although the literature on the chemical ecology of and use of semiochemicals to manage the Cerambycidae is growing, little is known about the chemical ecology of Cerambycidae fauna in Peru. Trapping studies that screen known attractants in off‐shore mitigation programs can provide valuable baseline knowledge to inform management of species introduced outside their native range. Known Cerambycidae pheromones were screened for activity in a year‐long field study in Peru to look for activity in the local Cerambycidae fauna. The most frequently captured species were Megacyllene andesiana (Casey), Oreodera bituberculata Bates, Aegomorphus longitarsis (Bates) and Discopus eques Bates. The activity period of A. longitarsis , O. bituberculata and D. eques occurred in mid‐September 2020 and for M. andesiana occurred in early October 2020. Responses to anti ‐2,3‐hexanediol, fuscumol and fuscumol acetate by M. andesiana , O. bituberculata and D. eques were observed. We observed antagonism of the responses of M. andesiana , O. bituberculata and D. eques when anti ‐2,3‐hexanediol, fuscumol and fuscumol acetate were tested in blends.
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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 | 0.001 | 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) | 0.003 | 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".