Data for the manuscript "Climatic suitability and invasion risk of the elm zigzag sawfly in North America"
Bibliographic record
Abstract
The elm zigzag sawfly, Aproceros leucopoda (Hymenoptera), native to eastern Asia, is among the most concerning potential pests for elm trees (Ulmus spp.); elms form an important component in many North Temperate forests. This sawfly species invaded Europe in 2003 and spread rapidly across much of that continent. In 2020, it was recorded in North America, and it has since become established in several parts of the eastern United States and Canada. Sawfly infestations can cause severe elm tree defoliation, branch die-back that weakens tree health, and potentially tree mortality. Sawfly invasions have the potential to further exacerbate elm decline, especially in conjunction with other pressures, including Dutch elm disease (Ophiostoma ulmi). We used rigorous approaches from distributional ecology to explore climatic suitability for A. leocopoda across North America, considering various sources of uncertainty in the data. We found that, without control, the elm zigzag sawfly could establish populations across eastern Canada, much of the central-eastern and northeastern United States, as well as in the Pacific Northwest. More southern areas of North America were not climatically suitable for this species. Predicted suitable areas for the sawfly overlap broadly with elm distributions, highlighting the need to control this invasion to mitigate potential economic and environmental impacts.
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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.665 | 0.260 |
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".