Prediction of current and future potential distributions of the <i>Eucalyptus</i> pest <i>Leptocybe invasa</i> (Hymenoptera: Eulophidae) in China using the CLIMEX model
Bibliographic record
Abstract
BACKGROUND: The wasp Leptocybe invasa Fisher & LaSalle (Hymenoptera: Eulophidae), a Eucalyptus (Myrtaceae) pest native to Australia, has caused economic and ecologic losses in China. It is a serious pest in southern provinces. Because climate is a limiting factor in insect distribution, we used the model CLIMEX to predict the effect of climate change on potential current and future distributions of L. invasa in China. Data were collected on the current locations of this wasp, along with the damage incurred to Eucalyptus. These data were used to create a forecast model to predict potential current and future distribution maps of L. invasa in China. RESULTS: The verification results showed that 99.5% of the distribution samples formulated by the model are highly reliable and accurate. The result predicted that the potential current distribution of L. invasa will concentrate south of the Yellow River basin. The future distribution maps predicted a small-scale potential expansion north-northwest of Guangxi and more areas within China will provide increasingly suitable habitats for colonization by L. invasa. CONCLUSION: These distribution predications will be useful in determining where preventive and control measures should be implemented against this pest wasp in Eucalyptus throughout China. © 2019 Society of Chemical Industry.
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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 | 0.000 |
| 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".