Multi-model assessments to characterize occurrences of emerald ash borer (Coleoptera: Buprestidae)
Bibliographic record
Abstract
Introduction and spread of nonindigenous species present a formidable threat to forest health. The emerald ash borer (EAB), Agrilus planipennis, is an East Asian-origin insect that has devastated ash (Fraxinus spp.) trees across the United States and parts of Canada since 2002. Proactive surveillance using high-performing predictive models could aid in mitigating pest risk. Predictor variables and modeling methods are important considerations in such analysis. Therefore, we assessed whether relevant single predictors, a combination of predictors grouped under a certain driver category, or multiple key predictors comprising several drivers, alter the goodness-of-fit of logistic regression models to EAB occurrence data (2002 to 2018) from Canada. The predictors used in models included spatial, topographic/positional, transport pathways/human hotspots, host-related factors, and climate-related variables. Using predictors from the best candidate logistic regression model, we tested the performance of 7 different model types including an ensemble model. Our findings showed that predictors from a wide range of drivers better characterized EAB occurrences than single predictors or a combination of predictors from any given driver category. In multi-model comparisons, random forest outperformed all other models, including the ensemble model. Elevation, infestation pressure, accumulated degree-days (>10 °C), and human population density were important predictors of EAB presence. Random forest and ensemble model forecasts for the city of Edmonton, Alberta, Canada, indicated an area of potential concern for EAB. Our research strongly underscores the utility of comparative multi-model approaches in invasive risk assessments that could have important implications for pest surveillance and management.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".