Prediction and Evaluation of Suitable Habitat of Invasive Vespa mandarina in North America based on MaxEnt Niche Model
Bibliographic record
Abstract
Vespa mandarina is one of the largest and most dangerous wasps in the world. Its invasion will bring great challenges to the local ecological balance and environmental security. Based on 8 climatic factors and 6 geographical factors, the suitable habitat, migration trend and living conditions of this species in North America were predicted and analyzed by MaxEnt niche model. The results showed that precision of cold quarter, min temperature of cold month and mean temperature of cold quarter were the three most important environmental factors affecting the distribution of Vespa mandarina; The most suitable climatic conditions for the survival of this species are the average annual temperature of 23.3 ℃ - 27.8 ℃, the precipitation in the driest season of 370mm-720mm and the precipitation in the coldest season of 850mm-1600mm; At the same time, the most suitable geographical environment for its survival is the coniferous forest or broad-leaved forest with an altitude of - 20m-100m, loam or sandy soil and a coverage rate of more than 75%; At present, the suitable areas of Vespa mandarina is mainly distributed in temperate or tropical areas such as the western coast of Canada, the northwest and eastern coast of the United States and Latin America, and the suitable area of Vespa mandarina will decrease under various CO2 emission scenarios in the future; In the future, the species will mainly start from the west coast of Washington state and migrate along the coastline to Alaska in the north and the Central American isthmus in the south.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".