1 A Decision Support System for Emerald Ash Borer Eradication Using Spatial-Dynamic Modeling
Bibliographic record
Abstract
Recently, an invasive Asian beetle known as the Emerald Ash Borer (EAB) (Agrilus planipennis Coleoptera: Buprestidae) has emerged as a threat to Ash trees in the Midwestern United States and Canada (McCullough and Katovich 2004). Significant infestations in Michigan and nearby areas have all but doomed nearly one billion native ash trees. This paper presents an argument for the establishment of a widely accessible knowledgebase of information on the EAB’s spread capabilities. We argue that spatial dynamic modeling stands as a flexible and powerful decision support system platform. We present initial simulations of EAB spread scenarios constructed using tree information and land use data collected for DuPage County, IL, an uninfected suburban county in the Chicago metropolitan area. These simulations test policies focused on impeding the costly spread of the beetle. This analysis also presents a framework for further studies assessing the economic impacts on municipalities and counties due to tree removal costs and aesthetic damage. Our work points to human driven movement as the major vector for EAB spread throughout our study area. Here, the focus falls on the ability of state and county implemented firewood quarantines to act as effective policies for slowing EAB spread.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".