Modeling potential movements of the emerald ash borer: the model framework
Bibliographic record
Abstract
The emerald ash borer (EAB, Agrilus planipennis Fairmaire) is threatening to decimate native ashes (Fraxinus spp.) across North America and, so far, has devastated ash populations across sections of Michigan, Ohio, Indiana, and Ontario. We are attempting to develop a computer model that will predict EAB future movement by adapting a model developed for the potential movement of tree species over a century of climate change. We have two model variants, an insect-flight model and an insect-ride model to assess potential movement. The models require spatial estimates of EAB abundance and ash abundance. The EAB abundance map shows a zone of initial infestation in the western suburbs of Detroit, with ash trees first dying about 1998. The fine-scale (270-m cells) ash basal area maps show highly variable values, but woodlots often have very high levels of ash. At the coarse scale (20-km cells) for the Eastern United States, available ash is high throughout the northern part of the country. With the flight model, probability of movement is dependent on EAB abundance in the source cells, the quantity of ash in the target cells, and the distances between them. With the insect-ride model, we used geographic information system data to weight factors related to potential human-assisted movements of EAB-infested ash wood or just hitchhiking insects. We are developing a gravity model that considers traffic volumes and routes between EAB source areas and various distances to campgrounds. Preliminary results from a test strip through northern Ohio show (1) the insect-flight model creates a relative probability of colonization that decreases quickly from the EAB range boundary edge; and (2) the insect-ride model provides occasions for long-distance transport via humanaided dispersals.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".