Effect of semiochemicals and trap height on catch of <i><scp>N</scp>eocerambyx raddei</i> in Jilin province, China
Bibliographic record
Abstract
Abstract We conducted experiments in Jilin, China, in 2011 and 2014 in forest stands dominated by mature Q uercus mongolica Fisch. ex Ledeb. ( F agaceae) to test the effects of longhorn beetle pheromones, plant volatiles, and trap height on catch of N eocerambyx raddei (Blessig & Solsky) (formerly M assicus raddei ) ( C oleoptera: C erambycidae) in traps. Traps captured 276 specimens of N . raddei in 2011 and 379 specimens in 2014 (384 females, 271 males). Ethanol was attractive to female but not male N . raddei . However, N . raddei was not attracted to any of the longhorn beetle pheromones tested, which included racemic 3‐hydroxyhexan‐2‐one, racemic 3‐hydroxyoctan‐2‐one, syn ‐2,3‐hexanediols, anti‐ 2,3‐hexanediols, racemic E , Z‐ fuscumol, racemic E , Z‐ fuscumol acetate, and monochamol, nor was it attracted to 2‐methyl‐3‐buten‐2‐ol. Traps placed in the tree canopy captured significantly more beetles than did traps in the understorey. Our results suggest that surveys for N . raddei should use ethanol‐baited traps placed in the tree canopy. If sex or aggregation pheromones are identified for N . raddei in the future, we predict that attraction to them will be enhanced by the presence of ethanol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".