Spatial distribution characteristics and accessibility of national wetland parks in China
Bibliographic record
Abstract
By using GIS and the methods of nearest neighbor index,Ripley's K function,and kernel density estimation,the spatial distribution of 298 China's national wetland parks was analyzed. Based on the matrix raster data,the spatial accessibility of China's national wetland parks at county-level was calculated by using the cost weighted distance method and ArcGIS as platforms. The spatial differences of county-level accessibility of the national wetland parks were analyzed by the exploratory spatial data analysis( ESDA). Results show that the national wetland parks generally exhibit an aggregated distribution. There is quite difference of spatial distribution of national wetland parks among both inter-provinces and inter-economic regions. The average accessibility is about 144. 07 min,and the area with the accessibility of national wetland parks within 120 min reaches 60%,while the area with the accessibility within 30 min accounts for13. 29%,and the longest time needs 1283 min for one park located at central Tibetan Plateau.Moreover,the distribution of the accessibility coincides with that of traffic lines. At county level,the estimated value of Moran's I is positive. National wetland parks and adjacent areas show a strong positive correlation. The distribution of hot spots regarding the accessibility shows an obvious hot spots- sub- hot spots- sub- cold spots- cold spots distribution pattern from east to west. Service range of each national wetland park is more advanced in western regions than that in eastern and central 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".