Nonlinear characteristics of the vegetation change and its response to climate change in the karst region of southwest China
Bibliographic record
Abstract
The vegetation is known to be sensitive to both climate change and anthropogenic disturbance. However, the relationship between changes in vegetation and climate is unclear in karst regions. The nonlinear characteristics of vegetation change and its possible relationships with driving factors in the karst region of southwest China are revealed, using methods of Ensemble Empirical Mode Decomposition, Mann-Kendall, and Partial Least Squares Regression. The results show that: (1) vegetation changes demonstrate an increasing trend with an abrupt change in 2002. Multiple time scales of 3, 6, 10, and 25-year are observed in vegetation variations, dominated by long-term trend and the short time scale of 3-year with variance contributions of 58.10% and 28.63%. (2) The relationship of climate indexes with vegetation changes shows r 2 = 0.78 ( p < 0.01) based on the reconstruction of characteristic scales, indicating significant great relationship. In space, the area percentage with relationship of climate to vegetation is more than 50%, and the impact is much greater after the abrupt change of vegetation in 2002 ( r 2 are 0.24–0.91 and 0.42–0.99, respectively). In addition, the correlation between vegetation change and ecological engineering is 0.15 ( p < 0.01). The results indicate that climate change is the main impact factor of vegetation change, ecological engineering has positive influences in improving vegetation condition, and methods of scales decomposition and abrupt detection could reveal some hidden information for better understanding ecosystems in karst regions.
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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 | 0.000 |
| 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 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".