The development of green open spaces and ecological patterns on the ecological supply chain and its implications for environmental sustainability
Bibliographic record
Abstract
This study aims to explore the interrelationships between Green Open Spaces, Ecological Patterns, the Ecological Supply Chain, and Environmental Sustainability in urban ecosystems. A quantitative research approach employing a cross-sectional design was utilized. Data were collected through surveys and field observations from urban residents with access to green spaces. Structural Equation Modeling (SEM) with Smart PLS was used for data analysis. The findings indicate significant impacts of Green Open Spaces and Ecological Patterns on both the Ecological Supply Chain and Environmental Sustainability. Moreover, the Ecological Supply Chain mediates the relationship between Green Open Spaces/Ecological Patterns and Environmental Sustainability. Limitations include the focus on a specific geographical area and potential biases in self-reported data. This study contributes to ecological theory by emphasizing the interconnectedness between ecological elements and their influence on Environmental Sustainability. Practically, it provides insights for urban planning and conservation efforts, highlighting the importance of preserving natural habitats within urban environments. The findings also underscore the need for holistic approaches to ecosystem management and sustainability. The novel aspect of this study lies in its examination of the mediating role of the Ecological Supply Chain in the relationship between ecological elements and Environmental Sustainability, offering new insights into the mechanisms driving ecosystem dynamics in urban settings.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| 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".