Human Activity Intensity and Its Spatial-Temporal Evolution in China’s Border Areas
Why this work is in the frame
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Bibliographic record
Abstract
Monitoring human activities in border areas is challenging due to the complex geographical environment and diverse people. China has the longest terrestrial boundary and the highest number of neighboring countries in the world. In this study, a human activity intensity index (HAI) was proposed based on land cover, population density, and satellite-based nighttime light for a long-term macroscopic study. The HAI was calculated at 1 km resolution within the 50 km buffer zone of China’s land boundary on each side in 1992, 2000, 2010, and 2020, respectively. Results show that human activity is low in about 90% of the study area. Overall, the HAI on the Chinese side is higher than that on the neighboring side, and the intensity of land use on the Chinese side has increased significantly from 1992 to 2020. Among China’s neighbors, India has the highest HAI with the fastest growth. With the changes in the HAI between China and its neighboring countries, four regional evolution patterns are found in the study area: Sino-Russian HAI decline; Sino-Kazakhstan HAI unilateral growth; Indian HAI continuous growth; China and Indochina HAI synchronized growth. Hotspot analysis reveals three spatial evolution patterns, which are unilateral expansion, bilateral expansion, and cross-border fusion. Both the “border effect” and “agglomeration effect” exist in border areas. The HAI changes in border areas not only impact the eco-environment but also affect geopolitics and geoeconomics. The HAI can be used as an instrument for decision-making and cooperation between China and neighboring countries in such areas as ecological protection, border security, and border trade.
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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) | 0.002 | 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 it