Detection and sampling of emerald ash borer (Coleoptera: Buprestidae) infestations
Bibliographic record
Abstract
Abstract Emerald ash borer, Agrilus planipennis Fairmaire (EAB) (Coleoptera: Buprestidae), has caused devastating levels of mortality to ash trees ( Fraxinus Linnaeus, 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".