Global invasion risk assessment of Lantana camara, a highly invasive weed, under future environmental change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Invasion risk assessments are essential for making informed decisions, allocating resources, and implementing targeted strategies to prevent or minimize the harmful effects of invasive species on native biodiversity, agricultural productivity, and natural ecosystems. In this study, the random forest algorithm was used to assess the spatial invasion risk of Lantana camara , one of the world’s top 100 worst invasive weeds, across all continents under current and future environmental conditions. The current invasion risk was relatively high on four continents (i.e., Africa, Australia, Oceania, and South America) within approximately 35°N and 35°S latitude, estimated to cover at least 68.98 % of the total land surface. Furthermore, projections for future environmental changes suggested a substantial increase in invasion risk across all continents, with the most significant changes (251.52 %) observed in Europe compared with current invasion levels. Additionally, invasion risk was predicted to extend beyond 35°N latitude. Categorizing 200 countries and territories into distinct risk levels, 27 countries had current invasion potential, and introduction and establishment was predicted in 114 countries. Moreover, at least 45 countries, including Canada, India, Italy, and United States, were projected to transition from no or low invasion risk to high invasion risk and 28 countries had a risk increase of over 50 %. Current study provides valuable insights into the global invasion risk posed by L. camara . These results are expected to be of great utility for invasive weed management , facilitating the development of control and sustainable management strategies for this notorious weed at both global and local scales.
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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 | 0.000 |
| 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 it