A Novel Analytical Framework for Understanding Human Influence in Shaping Vegetation Coverage Patterns: Insights From Yan'an, China
Bibliographic record
Abstract
ABSTRACT To restore and rebuild degraded ecosystems, the Chinese government has implemented a series of ecological projects that have led to significant increases in vegetation coverage and transformations in landscape patterns. Traditional surface indices, such as the vegetation coverage degree (VCD), primarily reflect natural resource endowments and fail to adequately capture the influence of human interventions, thereby limiting their effectiveness in detecting changes resulting from restoration efforts. To address this gap, we aimed to develop a comprehensive analytical framework by introducing a human‐activity‐sensitive indicator, the vegetation coverage potential achievement degree (VCPAD). This framework fully considers the impacts of natural resource endowments and human efforts on vegetation coverage patterns. The results show that in Yan'an, China, VCD has been improving since the implementation of the grain for green (GFG) project, but its spatial pattern consistently exhibits north–south differences. In contrast, although the VCPAD initially exhibited high positive spatial autocorrelation, its strength gradually weakened over time. These observations suggest that the VCD patterns are strongly determined by resource endowments, whereas those of the VCPAD are much more sensitive to changes driven by human activities. Finally, through a joint evolution analysis of Moran's I of both VCD and VCPAD, it was found that 13 county‐level units in Yan'an could be categorized into forest zones, traditional farming zones, and agropastoral zones. Overall, this study offers a novel and effective framework for understanding and evaluating the effects and impacts of ecological projects from a spatial pattern perspective, providing a foundation for the ecological restoration zoning and the formulation of localized vegetation restoration measures.
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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".