An improved trap for large wood-boring insects, with special reference to <i>Monochamus scutellatus</i> (Coleoptera: Cerambycidae)
Bibliographic record
Abstract
Commercially available multiple funnel traps have three potential limitations for trapping large wood-boring insects: (i) escape by captured insects from the dry collecting cup, (ii) low catches of insects that fall outside the trap, and (iii) poor visual orientation to the narrow funnel column. To test the importance of these limitations, we compared conventional multiple funnel traps to multiple funnel traps with water-filled collecting cups or large bottom funnels and to crossvane traps with a prominent silhouette. The experiment was conducted in a mill yard in the southern interior of British Columbia between 5 July and 2 October 2000. Differences in catch among different trap types indicated that the first and third of the three potential limitations were important for the capture of most target species. Crossvane traps captured significantly greater numbers of most Cerambycidae and Siricidae, and similar numbers of most Buprestidae, compared with the other traps. Of the two most abundant species, Xylotrechus longitarsus Casey was captured in consistently greater numbers in crossvane than in other traps, but Monochamus scutellatus (Say) showed little discrimination early in the flight season and much higher captures in crossvane traps late in the season. The change in behaviour of M. scutellatus may be related to a transition from maturation feeding to searching for oviposition sites.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".