Distinguishing the Impacts and Gradient Effects of Climate Change and Human Activities on Vegetation Cover in the Weihe River Basin, China
Bibliographic record
Abstract
Abstract Vegetation cover is crucial for ecosystem stability, responding sensitively to climate change and human activities, and is prone to irreversible degradation. However, the mechanisms driving vegetation variations due to natural and anthropogenic factors still need to be fully understood. This study focused on the Weihe River Basin to elucidate the response mechanism of vegetation cover change to climate change and human activities from 2001 to 2020. Long time‐series multi‐source data were combined with a pixel dichotomy model, Theil–Sen median trend analysis, and Mann‐Kendall test to examine the trends and delineate five gradients in vegetation cover change. Additionally, Extreme Gradient Boosting, the Shapley value, and a structural equation model were employed to investigate the multidimensional response of vegetation cover in the basin as a whole and different vegetation cover gradients. The results revealed a general upward trend in vegetation coverage in the Weihe River Basin from 2001 to 2020. Topographic conditions and human activities were identified as primary influencers. Notably, accounting for climate change, particularly about changes in maximum climatic variables, was found to be essential, with temperature changes exerting a greater impact on vegetation cover variations compared to precipitation changes. The interaction between human activities, climate change, and topographic conditions can alter the intensity of each factor’s effect. The direction of indicators mentioned above varied across the vegetation cover gradients, emphasizing the need for localized strategies to improve vegetation. These findings offer valuable insights into ecological protection and vegetation restoration in the Weihe River Basin.
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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 | 0.000 |
| 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) | 0.000 | 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 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".