Review of Ecosystem Level Impacts of Emerald Ash Borer on Black Ash Wetlands: What Does the Future Hold?
Bibliographic record
Abstract
The emerald ash borer (EAB) is rapidly spreading throughout eastern North America and devastating ecosystems where ash is a component tree. This rapid and sustained loss of ash trees has already resulted in ecological impacts on both terrestrial and aquatic ecosystems and is projected to be even more severe as EAB invades black ash-dominated wetlands of the western Great Lakes region. Using two companion studies that are simulating short- and long-term EAB infestations and what is known from the literature, we synthesize our current limited understanding and predict anticipated future impacts of EAB on black ash wetlands. A key response to the die-back of mature black ash will be higher water tables and the potential for flooding and resulting changes to both the vegetation and animal communities. Although seedling planting studies have shown some possible replacement species, little is known about how the removal of black ash from the canopy will affect non-ash species growth and regeneration. Because black ash litter is relatively high in nitrogen, it is expected that there will be important changes in nutrient and carbon cycling and subsequent rates of productivity and decomposition. Changes in hydrology and nutrient and carbon cycling will have cascading effects on the biological community which have been scarcely studied. Research to address these important gaps is currently underway and should lead to alternatives to mitigate the effects of EAB on black ash wetland forests and develop management options pre- and post-EAB invasion.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".