Efficacy of semiochemical-baited traps for detection of longhorn beetles (Coleoptera: Cerambycidae) in the Russian Far East
Bibliographic record
Abstract
The efficacy of various combinations of pheromones and plant volatile lures for detection of longhorn beetles (Coleoptera: Cerambycidae) in traps was tested in field bioassays in a mixed coniferous-deciduous forest near Vladivostok in the Russian Far East in 2009 and 2010. Traps detected 30 species (490 specimens) in 2009 and 23 species (182 specimens) in 2010. Overall, 38 longhorn beetle species were detected, with 15 species common to both years. Species composition differed among lure treatments, but the number of species detected with any single lure did not vary significantly among lures (12-16 species per lure in 2009; 3-10 species per lure in 2010). Type of lure significantly affected mean catch per trap of five species in 2009 and 2010. For these same species, lure type also significantly affected the efficacy of detection, i.e., the proportion of traps that captured at least one specimen of a given species. The combination of racemic E-fuscumol and spruce blend (a blend of five monoterpenes) positively affected mean catch of Tetropium castaneum (L.). Racemic 3-hydroxyhexan-2-one (K6), alone or combined with ethanol, increased mean catch of Anaglyptus colobotheoides Bates. The combination of K6 and ethanol increased mean catch of Phymatodes testaceus (L.), and the combination of racemic 3-hydroxyoctan-2-one and ethanol increased mean catch of Molorchus minor (L.). Use of longhorn beetle pheromone lures in trapping surveys increases the mean catch and probability of detecting certain species of Cerambycidae, including those that may be exotic and potentially invasive. Sample-based rarefaction indicated that eight traps per site were insufficient to detect all of the longhorn species potentially attracted by any individual lure treatment, i.e., species accumulation curves failed to reach an asymptote in most cases.
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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".