Karst landscapes of China: patterns, ecosystem processes and services
Bibliographic record
Abstract
Abstract Context The karst region of southwestern China, one of the largest continuous karsts in the world, is known for its unique landscapes and rich biodiversity. This region has suffered severe environmental degradation (e.g., vegetation cover loss, soil erosion and biodiversity loss). In recent decades, Chinese governments at different levels have initiated several ecological programs (e.g., Green for Grain, Mountain Closure) to restore the degraded environment and to alleviate poverty. Objectives This study summarizes landscape studies of karst landscapes patterns, their dynamics and interactions among landscape pattern, hydrological processes and ecosystem services (ES). Methods We conducted a systematic literature review of science and land use policy to identify knowledge gaps and recommend future research and policy directions. Results Karst landscapes have experienced rapid turnover in recent decades due largely to the overlap of intense human activity on the fragile karst ecosystems. Many studies have comprehensively examined hydrology, soil processes and ecosystem services (ES) and their relationships with landscape pattern. Most of these studies have found that karst ecosystems recover with improved ES. However, the importance of epikarst in hydrological and soil processes, intense anthropogenic disturbance and landscape heterogeneity in landscape models remains elusive. Conclusions Future research should focus on in-depth examination and modelling of karst specific hydrological and soil processes, investigating relationships between climatic change, landscape change, ecological processes, and region-specific ES assessments. Results from such research should provide the necessary scientific support for a comprehensive, national karst rocky desertification treatment project (Stage II) and poverty alleviation initiatives.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".