Assessing the ecological niche and invasion potential of the Asian giant hornet
Bibliographic record
Abstract
Abstract The Asian giant hornet ( Vespa mandarinia ) is the world’s largest hornet. It is native to East Asia, but was recently detected in British Columbia, Canada, and Washington State, USA. Vespa mandarinia are an invasion concern due to their potential to negatively affect honey bees and act as a human nuisance pest. Here, we assessed effects of bioclimatic variables on V. mandarinia and used ensemble forecasts to predict habitat suitability for this pest globally. We also simulated potential dispersal of V. mandarinia in western North America. We show that V. mandarinia are most likely to invade areas with warm to cool annual mean temperature but high precipitation, and could be particularly problematic in regions with these conditions and high levels of human activity. We identified regions with suitable habitat on all six continents except Antarctica. The realized niche of introduced populations in the USA and Canada was small compared to native populations, implying high potential for invasive spread into new regions. Dispersal simulations showed that without containment, V. mandarinia could rapidly spread into southern Washington and Oregon, USA and northward through British Columbia, Canada. Given its potential negative impacts, and the capacity for spread within northwestern North America and worldwide, strong mitigation efforts are needed to prevent further spread of V. mandarinia .
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".