Modeling the distribution of hemlock woolly adelgid under several climate change scenarios
Bibliographic record
Abstract
The hemlock woolly adelgid (HWA), Adelges tsugae Annand, has invaded eastern North America and caused significant mortality to eastern hemlock, Tsuga canadensis (L.) Carrière. In eastern North America, HWA’s range is theorized to be limited by minimum winter temperatures. As climate change reduces the severity of winter, the risk of northward expansion of HWA increases. This study used maximum entropy species distribution modeling in conjunction with HWA occurrence records and future climate projections to model habitat suitability for HWA throughout the range of eastern hemlock. Species distribution models were created for present and future climatic conditions using both historical climatic data and future climatic emissions scenarios for mid- and late-century. In addition, present climatic condition reference models for western North America and Asia were generated for comparison with HWA’s current range and earlier predictions of the potential range in eastern North America. Under a low emissions scenario, HWA will be capable of invading almost the entire range of eastern hemlock by the end of the century. More extreme warming scenarios result in a more rapid northwards shift by mid-century. The consequences for eastern hemlock are significant, with infestations likely to become more widespread and severe due to climate change.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".