Recreational Services from Green Space in Beijing: Where Supply and Demand Meet?
Bibliographic record
Abstract
Green space, mainly forests, shrubs, and grasslands, provides essential ecosystem services for human well-being. Based on multi-source data and using the Maximum Entropy model and Geographical Information System (GIS) tools, this research comprehensively assesses the supply and demand of recreational services from green space in Beijing. The supply of recreational services in Beijing is influenced by natural and human factors, showing large spatial variability. The supply level of mountainous areas with good natural geographical conditions and intact ecological landscape is significantly higher than that of plain areas with reduced vegetation and overexploitation. Residents have a high demand for recreational services in green space landscape and low demand in non-green space landscape. The quantitative balance pattern of supply and demand varies greatly, and most areas show the state of undersupply. The spatial matching pattern of supply and demand varies significantly too, and the mismatch is apparent. Spatial allocation should be more carefully considered than the aggregated supply and demand. Differentiated development strategies such as ecological reshaping, ecological development, restoration, and protection should be implemented for different areas in the future of planning and management in urban green areas. This will optimize and balance the supply-demand matching pattern for recreational services and promote the effective improvement of ecosystem service functions and residents’ ecological welfare.
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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) | 0.002 | 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 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".