The Identification and Driving Factor Analysis of Ecological-Economi Spatial Conflict in Nanjing Metropolitan Area Based on Remote Sensing Data
Bibliographic record
Abstract
The rapid socio-economic development of the metropolitan area has led to the continuous deterioration of the ecological environment. This leads to intense competition and conflict between different spatial use types. Spatial conflict research is essential to achieve ecological-economic coordination and high-quality development. However, existing studies lack comprehensive and direct ecological-economic spatial conflicts, especially those on the spatial-temporal evolution and potential drivers of spatial conflict. In this study, we identified the ecological-economic spatial conflicts in the Nanjing metropolitan area in 2010, 2015, and 2020. This study used the random forest to analyze the factors that influenced the change of spatial conflict. Results show that: (1) From 2010 to 2020, the ecological-economic spatial conflict in the Nanjing metropolitan area changed significantly. (2) Land use change has an important effect on spatial conflicts, which are easily triggered by uncontrolled urban expansion, but ecological land can mitigate spatial conflicts. (3) Relevant driving factors of spatial conflicts show multi-level features, so the development of conflict reconciliation countermeasures needs to be tailored to local conditions. This study provides a significant foundation for the high-quality development of the Nanjing metropolitan area and provides a reference for the planning and management of the territorial space.
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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 | 0.001 | 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".