Ground and Stem Sampling as Potential Detection Tools for the Wool of <i>Adelges tsugae</i> (Hemiptera: Adelgidae)
Bibliographic record
Abstract
The wool of the invasive, non-native hemlock woolly adelgid, Adelges tsugae Annand (Hemiptera: Adelgidae), occurs mostly on hemlock (Tsuga sp.) twigs, but can be dislodged from the canopy and end up on the forest floor and tree stem underneath the canopy. Herein, we developed and tested the efficacy of two novel sampling techniques, which are based on a visual examination of the forest floor and the hemlock stem for A. tsugae wool. Subsequently, we compared these two techniques to a visual examination of foliage and ball sampling which are two methods currently used operationally. We sampled 11 hemlock stands, with low to moderate incidence A. tsugae populations, near Ithaca, New York in 2016 and assessed the probability of detecting wool on a tree and in a stand, as well as the relative variation and relative net precision for each of the four techniques. We found that sampling the tree stem outperformed foliage and ground sampling, likely because of its higher detection rate and lower relative variation, but not ball sampling. Our findings suggest that combining stem, ball and ground sampling was the most effective combination of techniques and gave a high probability of detecting an infested tree or an infested stand. All techniques were an improvement over foliage sampling, even after increasing the foliage sampling effort fivefold.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".