Effects of Climate Change on Invasion Potential Distribution of Lantana camara
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Climate change appears to be affecting global patterns of invasive species distribution. Forecasts based on ecological niche modeling suggest that greater impacts can be expected in the future. However, such projections are contingent on assumptions regarding the future climate conditions and invasion potential of a species. This study explores the relationship between climate change and potential distribution of Lantana camara in the National Parks of Jim Corbett and Rajaji (Uttarakhand, India). Using three representative climate change models viz., CSIRO (Commonwealth Scientific and Industrial Research Organization), CCCMA (Canadian Centre for Climate Modeling and Analysis) and HadCM3 (Hadley Centre for Climate Prediction and Research’s General Circulation Model) across the time slices 2020 to 2080 under two regional climate change scenarios A2a and B2a, Lantana camara potential distribution models were derived. The model projections were in consensus that invasion range was likely to expand and infestation would be more severe under the A2a scenario indicating that the species may prefer warmer conditions. Taken together, the modeled results suggest that in the future, the two National Parks may be impacted largely by the gregarious presence of Lantana camara. Predictive models can provide resource managers with a tool for the early detection of invasive species and help circumvent negative ecological impacts resulting in substantial economic savings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".