How does the Emerald Ash Borer (<i>Agrilus planipennis</i>) affect ecosystem services and biodiversity components in invaded areas?
Bibliographic record
Abstract
Environmental risk assessment (ERA) is an important component of risk analysis for plant pests and invasive alien species (IAS), and a standardized and consistent methodology has recently been developed for evaluating their impact on ecosystem services and biodiversity. This paper presents the application of this innovative methodology for ERA to Agrilus planipennis, the emerald ash borer, which causes significant mortality to Fraxinus (ash) species in forests and urban areas of North America (here: USA and Canada, excluding Mexico) and Russia. The methodology follows a retrospective analysis and summarizes information and observations in invaded areas in North America and Russia. Uncertainty distributions were elicited to define quantitatively a general pattern of the environmental impact in terms of reduction in ecosystem provisioning, supporting and regulating services, and biodiversity components. The environmental impacts of A. planipennis are time‐ and context‐dependent, therefore two time horizons of 5 and 20 years after introduction and two ecosystems (urban and forest) were considered. This case study shows that the quantitative assessment of environmental impacts for IAS is both possible and helpful for decision‐makers and risk managers who have to balance control costs against potential impacts of IAS.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".