Spatio‐Temporal Change of Habitat Quality in Northeast China: Driving Factors Exploration Based on Land Use and Land Cover Change
Bibliographic record
Abstract
ABSTRACT Ecological environment plays an indispensable role in sustaining and developing human society and natural ecosystems, while it continually suffers from degradation caused by human activities. Land Use and Land Cover (LULC), which serves as a proxy of the intensity of human intervention, has been regarded as an equally important factor affecting habitat quality as climate change. Despite exploring the close relationship between LULC changes and habitat quality, current research remains largely theoretical and does not delve into management measures following habitat degradation. Consequently, its practical implications for ecological conservation are limited. In this study, taking Northeast China, which has a prominent contradiction between LULC and ecological protection, as the study area, the InVEST model was introduced to assess the habitat quality based on LULC data from 2000 to 2020. Then, the Geographically Weighted Regression (GWR) model was employed to analyze the explanatory variables of habitat quality change in terms of LULC change. The results indicated that LULC change in Northeast China from 2000 to 2020 mainly occurred between cultivated land, artificial land, grassland, and forestland. Habitat quality demonstrated a progressive decline yet remained at an intermediate level and exhibited significant spatio‐temporal heterogeneity on the whole. Furthermore, the regression results demonstrated there was a significant correlation between LULC change and habitat quality change. Finally, Northeast China was classified into three functional zones by the K‐Means clustering analysis: coordinated development zone, ecological conservation zone, and key ecological functional zone, each with its own characteristics and development priorities. The findings can provide a scientific reference for the rational use of land and zoning management of habitat quality in Northeast China.
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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".