Study on Field Trapping Efficacy of Different Semiochemicals on Four Pine Bark Beetles,Scolytidae
Bibliographic record
Abstract
With the synthetic semiochemical lures introduced from Canada and the Lingren funnel traps made in China,the effi-cacy of monitoring and control of semiochemicals such as alpha-pinene(AP),nonanal(NL),trans-verbernol(TV) and myrtenol(MT) was tested on four species of pine bark beetles in the field.The result showed that good efficacy for attracting Cryphalus fulvus,Tomicus piniperda,T.minor and Hylastes plumbeus was obtained.The amount [17.5 heads/(day.trap)] trapped Cryphalus fulvus with 2AP was significantly different from the control and other treatments,which was most effective,25 times as that of con-frontation.For the semiochemicals to Tomicus piniperda,T.minor and Hylastes plumbeus,the trapped amounts were also signifi-cantly different from the control.The trapping efficacy for Tomicus piniperda and T.minor with 2AP+NL+TV was highest,612 times and 1085 times respectively as that of the control.2AP+NL+MT+TV was also most effective to Hylastes plumbeus,136 times as that of the control.During the period of 2005~2006,the Cryphalus fulvus and Tomicus minor had higher population densities than other pine bark beetles in Qianshan Scenery Zone.
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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".