Evaluation of Trapping Schemes to Detect Emerald Ash Borer (Coleoptera: Buprestidae)
Bibliographic record
Abstract
Management responses to invasive forest insects are facilitated by the use of detection traps ideally baited with species-specific semiochemicals. Emerald ash borer, Agrilus planipennis Fairmaire, is currently invading North American forests, and since its detection in 2002, development of monitoring tools has been a primary research objective. We compared six trapping schemes for A. planipennis over 2 yr at sites in four U.S. states and one Canadian province that represented a range of background A. planipennis densities, canopy coverage, and ash basal area. We also developed a region-wide phenology model. Across all sites and both years, the 10th, 50th, and 90th percentile of adult flight occurred at 428, 587, and 837 accumulated degree-days, respectively, using a base temperature threshold of 10°C and a start date of 1 January. Most trapping schemes captured comparable numbers of beetles with the exception of purple prism traps (USDA APHIS PPQ), which captured significantly fewer adults. Trapping schemes varied in their trap catch across the gradient of ash basal area, although when considering trap catch as a binary response variable, trapping schemes were more likely to detect A. planipennis in areas with a higher ash component. Results could assist managers in optimizing trap selection, placement, and timing of deployment given local weather conditions, forest composition, and A. planipennis density.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".