Detection and sampling of emerald ash borer (Coleoptera: Buprestidae) infestations
Bibliographic record
Abstract
Abstract Emerald ash borer,Agrilus planipennisFairmaire (EAB) (Coleoptera: Buprestidae), has caused devastating levels of mortality to ash trees (FraxinusLinnaeus, Oleaceae) in North America. Early infestations of this insect are extremely difficult to detect due to cryptic larval feeding and lack of obvious signs or symptoms of initial attack. Considerable research has been conducted to develop tools and techniques aimed towards providing early detection and delimitation of populations of this invasive species. Sampling tools and techniques include: (1) relating visual signs and symptoms to the presence of EAB infestations; (2) use of girdled trap-trees to increase captures of adults and subsequent larval densities; (3) sub-sampling protocols to detect larvae under the bark based on their within-tree distribution; (4) artificial traps baited with pheromones and/or host volatiles attractive to adult EAB; (5) biosurvellience using buprestid-hunting wasps; and (6) remote sensing techniques. Additional research modelling patterns of infestation at the landscape scale indicate very clumped or aggregated distributions, greatly increasing the difficulty of early detection across large spatial scales. Further research is still required to increase the efficacy and efficiency of early detection tools and techniques, including cost/benefit analysis of the various sampling options, increased understanding of patterns of initial infestation across the landscape, development of sampling programs for both detection and delimitation, and development of sequential sampling programs to estimate EAB density. This information will enable foresters to make informed decisions regarding management strategies against this devastating pest.
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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) | 0.001 | 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".