Emerald Ash Borer Research: A Decade of Progress on an Expanding Pest Problem
Bibliographic record
Abstract
Beautiful, shady neighborhoods all over the Midwest and the Northeast are bare of their ash trees, cut down because of the emerald ash borer (EAB). The rapidly spreading EAB infestation has also set off a storm of scientific investigation into the ecological and social damage and the costs to affected communities. Since it was first detected in 2002 around Detroit and neighboring parts of Ontario, EAB has spread to 18 states, from Kansas City to Minneapolis/St. Paul in the Midwest, south to the Smoky Mountains National Park, and all the way north to New Hampshire and Montreal, Quebec. Since its arrival, EAB has been able to attack and kill all native species of North American ashes (genus Fraxinus) that it has encountered. Much of the long-distance spread of EAB is due to human activities—people moving infested ash firewood and nursery trees out of quarantined areas. Early eradication efforts consisted of cutting and chipping or burning infested wood and prevention efforts focused on quarantines, developing detection and treatment methods for individual trees, and education efforts such as the “Don’t Move Firewood ” campaign. Knowledge about the EAB and how to control it, or at least slow its spread, continues to drive efforts to save ash. Dead ash “skeletons ” create openings in canopy. Photo by Kathleen Knight, U.S. Forest Service continued on page two 1 continued from page one
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".