Sticky traps as an early detection tool for crawlers of Adelges tsugae (Hemiptera: Adelgidae)
Bibliographic record
Abstract
We developed an approach using sticky trap arrays as an early detection tool for populations of first-instar nymphs of the hemlock woolly adelgid (Adelges tsugae Annand), a pest of hemlocks (Tsuga spp. [Pinaceae]) in North America. We considered the detection rate of at least one nymph from trapping arrays consisting of one to six sticky panels, where we varied both the surface area of each trap that we assessed and the length of the trapping duration. We also estimated the time needed to set up, service, and assess groups of traps and attempted to relate capture of nymphs on traps to incidence and abundance of A. tsugae in the canopy above the traps. Arrays consisting of two traps provided a detection rate of 75% when 87.5% of the surface area of each trap was assessed, a process that required 38 min per array. The probability of detecting nymphs on traps left in the field for 5-6 d was similar to that for traps left for 12 d. The number of nymphs trapped in an array predicted the probability of finding A. tsugae in the canopy but only when all six traps were fully assessed. To reliably detect incipient A. tsugae infestations, we recommend placing arrays of traps at 1 km intervals along the perimeter of a stand during peak activity of first-instar sistentes nymphs and servicing these arrays every 5-7 d.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".