Vegetation responses to simulated emerald ash borer infestation in <i>Fraxinus nigra</i> dominated wetlands of Upper Michigan, USA
Bibliographic record
Abstract
The invasive emerald ash borer (EAB) (Agrilus planipennis Fairmaire (Coleoptera: Buprestidae)) is a significant threat to biodiversity and ecosystem processes in North American forests. Of particular concern is the fate of Fraxinus nigra (black ash), which is frequently a dominant canopy species across much of its range. To investigate the potential vegetation response to the loss of this foundation species, EAB-induced mortality was simulated in F. nigra dominated wetlands of Upper Michigan, USA. No growth response of residual overstory species occurred over the course of three growing seasons, which may in part be attributed to negative effects of post-treatment growing conditions, including prolonged inundation. A significant increase in non-Fraxinus sapling growth rate was observed, however. Mortality of F. nigra did not impact overall stem recruitment or regeneration, although species composition is shifting towards Acer rubrum (red maple) and Betula alleghaniensis (yellow birch) in the seedling layer. The herbaceous community exhibited the greatest response, nearly doubling in areal cover by the end of the study. Importantly, this expanded cover was not associated with decreased establishment of new woody seedlings, suggesting that increased competition between these functional groups has not yet impacted the potential for future recovery of woody vegetation in these forests.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".