Factors affecting catches of bark beetles and woodboring beetles in traps
Bibliographic record
Abstract
Abstract The use of semiochemical-baited traps for detection, monitoring, and sampling bark beetles and woodboring beetles (BBWB) has rapidly increased since the early 2000s. Semiochemical-baited survey traps are used in generic (broad community level) and specific (targeted toward a species or group) surveys to detect nonnative and potentially invasive BBWB, monitor established populations of invasive or damaging native species, and as a tool to survey natural communities for various purposes. Along with expansion in use, much research on ways to improve the efficacy of trapping surveys for the detection of specific pests as well as BBWB in general has been conducted. In this review, we provide information on intrinsic and extrinsic factors and how they influence the efficacy of detecting BBWB in traps. Intrinsic factors, such as trap type and color, and other factors are described, as well as important extrinsic factors such as habitat selection, horizontal and vertical placement, and disturbance. When developing surveys, consideration of these factors should increase the species richness and/or abundance of BBWB captured in traps and increase the probability of detecting nonnative species that may be present. During generic surveys, deploying more than one trap type or color, using an array of lures, and trapping at different vertical and horizontal positions is beneficial and can increase the number of species captured. Specific surveys generally rely on predetermined protocols that provide recommendations on trap type, color, lure, and trap placement.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".