An Analysis of Spatial Variation in Human Impact on Forest Ecological Functions
Bibliographic record
Abstract
As the cornerstone of terrestrial ecosystems, forests have faced mounting challenges due to escalating human activities, jeopardizing their vital ecological functions and even their existence. It has become an important issue to explore how to promote harmonious coexistence of man and nature, or even to improve the forest ecological function (FEF) through human activities. Thus, in this study, we select the Yellow River Basin (YRB) in China as a typical region. Firstly, we assess the FEF at the county level and reveal their spatial distribution and agglomeration characteristics on the basis of the data from the Ninth National Forest Inventory of China. Then, using multiple linear regression (MLR) and geographically weighted regression (GWR) modeling, we further explore the overall impacts of different human activities on FEF and their spatial differences, respectively. Our findings underscored a moderate deficiency in the county-level FEF in the YRB, with pronounced positive spatial agglomerations. The high–high areas are primarily clustered in the southern and central mountainous areas, whereas low–low areas are distributed in the upstream warm temperate steppe and desert-grassland regions. Human activities exert substantial impacts on FEF, with distinct spatial heterogeneity in the coefficient and significance levels. The trend analysis indicates that FEF is more sensitive to the increase in living land, population density and forest protection in the east–west direction. And in the north–south direction, FEF is more easily affected by agricultural development, population growth and urbanization. This study verifies that natural factors dominate FEF in those regions where human activities are quite scarce, and also reveals that due to the inter-constraint or counteract effects among different human activities, FEF may still ultimately depend on the natural endowments in some populated regions. We point out the core human activity factors affecting FEF after excluding the interference from natural conditions. And we recommend that policymakers prioritize sustainable development strategies that mitigate the adverse impacts of human activities on forest ecosystems while promoting conservation efforts tailored to the unique characteristics of each region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".