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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".