Towards pheromone‐based monitoring of nun moth, <i>Lymantria monacha</i> (L.) (Lep., Lymantriidae) populations
Bibliographic record
Abstract
The research objective was to develop pheromone‐based monitoring of the nun moth, Lymantria monacha (L.), an important defoliator of spruce and pine forests in central Europe. In 38 spruce or pine forests in central Europe, captures of male L. monacha in nonsaturating Unitraps and saturating Delta sticky traps baited with 0.2, 2, 20, or 200 μg of the L. monacha (pheromone) volatile blend [(±)‐disparlure, (±)‐monachalure, and 2‐methyl‐ Z 7‐octadecene at a 20 : 20 : 1 ratio] were compared with estimates of population densities obtained by counts of larval faecal pellets, pupal cases, and adult moths resting on tree trunks. Total captures of male L. monacha throughout the flight season in both types of trap were correlated with numbers of larval faecal pellets, irrespective of pheromone dose. Nonsaturating Unitraps baited with 2 μg of the L. monacha volatile blend seem to provide a cost‐effective tool for monitoring densities of L. monacha populations. Long‐term testing of this monitoring system has been initiated to substantiate the quantitative relationship between larval populations and trap captures of male L. monacha and to determine the threshold number of captured male moths that indicates an incipient outbreak.
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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) | 0.000 | 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".