Changes in ash tree demography associated with emerald ash borer invasion, indicated by regional forest inventory data from the Great Lakes States
Bibliographic record
Abstract
The emerald ash borer (EAB) ( Agrilus planipennis Fairmaire) is a nonnative phloem-feeding beetle that was accidentally introduced near Detroit, Michigan, two to three decades ago. North American ash ( Fraxinus spp.) exhibit little or no resistance, and as this insect species expands its range, extensive mortality results. Previous studies of the impacts of EAB, typical of most insect and disease impact studies, utilized data acquired from sites with known infestations and cannot be used to make regional estimates of change on forest land. By contrast, this study investigated the regional impacts of EAB on the affected resource using information from a large-scale forest inventory (Forest Inventory and Analysis program of the US Department of Agriculture, Forest Service) previously implemented to estimate regional forest resources. Results indicate that since 1980, ash has been increasing throughout many of the Great Lakes States but EAB is reversing this trend in recently invaded areas. Within 50 km of the epicenter of the EAB invasion, a major decline was observed after 2004. For growing stock (trees at least 12.7 cm diameter at breast height), average ash volume decreased from 12.7 to 3.2 m3·ha–1and mortality increased from 0.1 to 1.4 m3·ha–1·year–1on timberland between the 2004 and 2009 inventories.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".