A Forest Death in Three Acts: From Woodpeckers to Water, the Emerald Ash Borer's Effects are Widespread
Bibliographic record
Abstract
Dead treetops, their bare branches curving upward, stood dark against a blue sky. Only a few years before, these had been towering, vibrant ash trees, playing a vital role in riparian forest ecosystems of northwest Ohio. Then, the emerald ash borer (Agrilus planipennis), a shiny green beetle whose voracious maggot-like larva feeds just beneath the bark of the trees, swept through the forests leaving death and destruction in its wake. The emerald ash borer, or EAB, is native to eastern Asia. There, it has little impact on Asian ash species, which are able to mount defenses against the larvae. But in the late 1990s, it was accidentally introduced to North America, arriving near Detroit. Michigan, on wooden shipping material. When EAB reached the nearby forests, it encountered North American ash species that had no co-evolutionary history with the pest and almost no defense. The insect was inadvertently spread by people, moving on firewood, logs and even vehicles. Now, it occurs in 35 U.S. states and five Canadian provinces. Our job was to monitor the effects of this invasive pest. including the decline and death of the ash trees, and the cascading effects of the sudden loss of these tree species on their forest ecosystems.
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 | 0.000 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".