Bibliographic record
Abstract
Low carbon economy,sustainable development and employment problems have been the focus of world attention.With the prosperity of ecotourism,the forest park in China gets its all-around development.Forest park has been a leading role in the development of forest tourism industry.Through the development of forest tourism in forest park,people's awareness of ecological protection was improved,and economic development in the surrounding was promoted,which has an increasingly important role in promoting regional economic development.The study of the spatial structure of forest park is receiving increasing attention but methodology so far has used qualitative rather than quantitative methods.The change of accessibility plays a prominent role of motive force to promote the regional economic development and regional spatial structure changes.Evaluation of accessibility generally includes network analysis,grid analysis and raster analysis.Based on an investigation of 713National Forest Parks and using GIS and some quantitative analysis methods,such as Nearest Neighbor Index(NNI)and Hot spot clustering,the spatial structure of National Forest Parks was investigated,and their characteristics and distribution for different strategies were discussed.Based on matrix raster data covering the whole space,this paper calculated spatial accessibility of all counties in China using cost weighted distance method and ArcGIS as platforms.Then spatial differences of county accessibility of scenic spots were discussed by using ESDA(Exploratory Spatial Data Analysis).The results show that general National Forest Parks exhibited an aggregated distribution.Considering the accessibility,we find that the human scenic spots were more centralized.The average accessibility was about 60.5min,and the area where the accessibility of scenic spots was within 2hreached 63.29%,while the area where the accessibility was within 30minutes accounted for 19.84% and the area located at central Tibetan Plateau took the longest time 595min.The average accessibility was shortened from 168.18min in 1991to 137.86min in 2010.And then,distribution of the accessibility pointed to traffic line.At county level,the estimated values of Moran's I were all positive numbers using analysis of spatial association.All the test results indicate that National Forest Park and adjacent areas showed positive correlation.Distribution of hot spots regarding the accessibility showed an obvious hot spots–sub-hotspots– sub-cold spots-cold spots zonal distribution pattern from east to west.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".