Spacing of traps baited with species-specific <i>Lymantria</i> pheromones to prevent interference by antagonistic components
Bibliographic record
Abstract
Abstract In pheromone-based surveys for detecting multiple species of exotic lymantriine moths (Lepidoptera: Noctuidae: Lymantriinae), spacing between traps baited with species-specific pheromone lures must be sufficient to prevent antagonistic effects of heterospecific pheromone on lure attractiveness. Conducting field experiments with the Japanese gypsy moth, Lymantria dispar japonica Motschulsky, in northern Honshu, Japan, we first determined which congeneric pheromone components have strong antagonistic effects on attraction of male moths to the conspecific pheromone (7R,8S)-cis-7,8-epoxy-2-methyloctadecane ((+)-disparlure). Since the most antagonistic compounds were pheromone/volatile components from the sympatric nun moth, L. monacha (L.), we then conducted experiments with paired traps baited with either a L. dispar (L.) pheromone lure ((+)-disparlure (50 µg)) or L. monacha pheromone lure (a mixture of (7R,8S)-cis-7,8-epoxyoctadecane ((+)-monachalure (50 µg)), (7Z)-2-methyloctadecene (5 µg), and (+)-disparlure (50 µg)). As spacing between paired traps increased (0, 0.5, 2, 7.5, 15, or 30 m), the antagonistic effect of the L. monacha lure on the attractiveness of the L. dispar lure decreased and finally disappeared. For pheromone-based detection surveys of multiple species of exotic lymantriine moths in North America to be effective, trap spacing of 15 m is recommended.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".