Advances in understanding and managing insect pests of forest trees
Bibliographic record
Abstract
The emerald ash borer, Agrilus planipennis, is the most damaging invasive forest insect pest ever to have invaded North America. It is native to Asia and is established in the United States, Canada, European Russia and Ukraine where it threatens native ash across North America and Europe. We evaluated trap designs, colors, and lures for A. planipennis detection at sites with varying infestation levels. Purple or green sticky prism traps and multiple funnel traps hung in the canopy of ash trees and double-decker traps (consisting of two sticky prisms attached to a 3m vertical pole at 3m and 1.8m above ground) had high detection rates even at sites with very low infestation levels. At a low infestation site, females were more attracted to dark purple sticky prism traps hung in the canopy and toManuka oil and Phoebe oil lures than to light green sticky prismtraps or cis-3-hexenol lures; whereas, males were more attracted to light green sticky prism traps in the canopy and cis-3-hexenol lures than to dark purple sticky prism traps or Manuka and Phoebe oil lures.Moremales and females were captured in double-decker traps with dark green upper prisms and light purple lower prisms, baited with cis-3-hexenol, than in double-decker traps with dark purple upper and lower prisms. Dark green funnel traps and double-decker traps with dark green upper and light purple lower prisms baited with cis-3-hexenol lures captured more females than dark green sticky prism traps hung in the canopy at sites with very low infestation levels. Detection rates were similar among trap types and ranged from 75 to 80% for dark green sticky prism traps, 82.5–100% for dark green funnel traps, and 100% for double-decker traps with dark green upper and light purple lower prisms at sites with very low A. planipennis infestations. Cost, ability to reuse the traps, and ease of deployment varies among trap types. These and other factors including trap placement, host density and condition should be considered in selecting traps and designing operational surveys. Future research is needed to determine effective trapping radius, relationship of trap catches to population density, cost benefit of different trap types, and optimal deployment strategy. Greater numbers of A. planipennis captured and higher detection rates in cis-3-hexenol-baited double-decker traps with dark green upper prisms and light purple lower prisms and in dark green funnel traps compared to dark green prism traps at sites with very low infestation levels, suggest these trap types would be most effective for operational detection surveys.
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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 | 0.000 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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".