Geographic distribution and prediction of potential suitable regions of Iva xanthifolia
Bibliographic record
Abstract
Foreign invasion plant flase ragweed(Iva xanthifolia)is spreading rapidly in the Northeast of China,which threatens the natural ecosystem and does harm to people's health.By doing field work distribution of flase ragweed in Northeast China was detected.Maxent ecological niche models help to predict the potential suitable regions of flase ragweed both in China and the other parts of the world.And natural intermittent points classification method of ArcGIS helps to classify the risk regions.The receiver operating characteristic curve was applied to access the prediction accuracy.The main environmental variables were analyzed by Jackknife approach and response curve of environmental variables.The results showed that flase ragweed had a wildly suitable regions which continue to spread its areas.In China,the suitable distribution area mainly locates in the North,the Northeast,the East,the Central,the Southwest and the Northwest of China;most of the suitable regions were in Heilongjiang,Jilin,Liaoning,Shanxi,Shanxi,Henan,Hebei,Shandong,Beijing,Tianjin,Neimenggu and Xinjiang.In the world,risk regions existed worldwide except Antarctica;the risk regions mainly lie in North America(including Canada and America),Africa(Morocoo),Europe(Spain,Croatia,Hungary,Slovakia,Bosnia-herzegovina,Serbia,Macedonia,Bulgaria,Georgia,Armenia and Azerbaijan),and Asia(Turkey,Iran,Kazakhstan,Tajikistan,China,North Korea,South Korea and Japan).The above areas show that middle latitude regions with higher average annual temperature,the lowest in the coldest month or lower average temperature in coldest quarter are the most suitable for flase ragweed to grow.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".