Riparian forest conditions along three northern Michigan rivers following Emerald Ash Borer invasion
Bibliographic record
Abstract
Emerald Ash Borer (EAB; Agrilus planipennis Fairmaire (Coleoptera, Buprestidae)) has killed millions of ash (Fraxinus spp.) trees since its 2002 detection in southern Michigan and Ontario. Effects of EAB in riparian forests, where ash species are often abundant, could influence terrestrial and aquatic conditions but are largely unknown. We surveyed vegetation and coarse woody material within and between three gaps in riparian forests along stretches of three rivers in northwestern Michigan. Gaps were originally dominated by Fraxinus pennsylvanica Marsh. and Fraxinus nigra Marsh., which comprised 46%–71% of total basal area, while ash comprised <10% of basal area in forests between gaps. More than 95% of overstory ash were killed by EAB. Fraxinus pennsylvanica saplings were abundant in gaps but F. nigra regeneration was minimal. Few tree seedlings occurred in gaps, which were dominated by dense sedge mats. Aerial imagery showed the EAB invasion has resulted in 3.6–7.1 canopy gaps·km−1 and dead ash account for 13%–21% of total basal area within 100 m of the banks of all three rivers. Long-term monitoring will be useful to evaluate persistence of ash, particularly F. nigra, the stability of riparian buffers, and potential indirect effects of EAB invasion on the adjacent aquatic systems.
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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 | 0.001 | 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".