Detectability of the Emerald Ash Borer (Coleoptera: Buprestidae) in Asymptomatic Urban Trees By Using Branch Samples
Bibliographic record
Abstract
The emerald ash borer, Agrilus planipennis Fairmaire, is an exotic invasive insect causing extensive mortality to ash trees, Fraxinus spp., in Canada and the United States. Detection of incipient populations of this pest is difficult because of its cryptic life stages and a multiyear time lag between initial attack and the appearance of signs or symptoms of infestation. We sampled branches from open-grown urban ash trees to develop a sample unit suitable for detecting low density A. planipennis infestation before any signs or symptoms are evident. The sample unit that maximized detection rates consisted of one 50-cm-long piece from the base of a branch ≥6 cm diameter in the midcrown. The optimal sample size was two such branches per tree. This sampling method detected ≈75% of asymptomatic trees known to be infested by using more intensive sampling and ≈3 times more trees than sampling one-fourth of the circumference of the trunk at breast height. The method is less conspicuous and esthetically damaging to a tree than the removal of bark from the main stem or the use of trap trees, and could be incorporated into routine sanitation or maintenance of city-owned trees to identify and delineate infested areas. This research indicates that branch sampling greatly reduces false negatives associated with visual surveys and window sampling at breast height. Detection of A. planipennis-infested asymptomatic trees through branch sampling in urban centers would provide landowners and urban foresters with more time to develop and implement management tactics.
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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 | 0.000 | 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 | 0.000 |
| 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".