Improving Detection Tools for Emerald Ash Borer (Coleoptera: Buprestidae): Comparison of Multifunnel Traps, Prism Traps, and Lure Types at Varying Population Densities
Bibliographic record
Abstract
The emerald ash borer, Agrilus planipennis Fairmaire (Coleoptera: Buprestidae), is a serious invasive pest of North American ash (Fraxinus spp.) that has caused devastating mortality since it was first identified in North America in 2002. In 2012, we conducted field trapping assays that tested the efficacy of purple prism and fluon-coated green multifunnel (Lindgren funnel) traps. Traps were baited with combinations of several lures that were previously shown to be attractive to A. planipennis: manuka oil--a sesquiterpene-rich oil, (3Z)-hexenol--a green leaf volatile, or (3Z)-dodecen-12-olide [= (3Z)-lactone], a sex pheromone. Eighty-nine blocks (trap lines) were tested throughout nine states along the outer edges of the currently known A. planipennis infestation in North America. Trap catch was highest on fluon-coated green multifunnel traps, and trap detections at sites with low A. planipennis population density ranged from 72 to 76% for all trap and lure types tested. (3Z)-hexenol and (3Z)-lactone baited traps functioned as well as (3Z)-hexenol and manuka oil-baited traps. Independent of the lure used, detection rates on green fluon-coated multifunnel traps were comparable with glued purple prism traps in areas with low A. planipennis population densities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".