Revolutionizing ecological security pattern with multi-source data and deep learning: An adaptive generation approach
Bibliographic record
Abstract
• This study innovatively constructs a regional sustainable development framework based on ecosystem activity, sustainability, stability, and integrity, with the unique characteristic of “contribution-sensitivity-vigour-organization.” • This study employs an adaptive generation approach utilizing a Self-Organizing Map (SOM) to identify eco-sources. It enhances the comprehension of eco-sources' complexity by processing original information from diverse factors and overcomes the limitations of traditional overlay analysis. • This study constructs an Ecological Security Pattern for the Poyang Lake Ecological Urban Agglomeration and proposes an optimized “one ring, two corridors, two zones, multiple cores” pattern, along with practical policy recommendations. The development concept of “Ecological Life Community of Mountains, Rivers, Forests, Fields, Lakes, and Grass” for ecological civilization construction holds substantial practical significance for the balanced advancement of regional economy, social development, and ecological environment. Constructing an ecological security pattern (ESP), a significant strategic initiative for ecological civilization-building, is essential to balance protection and development and explore a harmonious coexistence between humans and nature. However, traditional research methods have limitations using overly simplistic indicators and the overlay analysis method in identifying ecological sources, in their ability to discern the original information contained in various factors and can only identify homogenous ecological sources. Accordingly, taking the Poyang Lake Ecological Urban Agglomeration (PLEUA) as an example, this study constructs an innovative framework for regional sustainable development based on the perspectives of ecosystem health, integrity, and ecosystem services association, characterized by “contribution-sensitivity-vigour-organization”. An adaptive generation approach utilizing deep learning, specifically the self-organizing mapping neural network model, is employed to overcome the traditional homogenisation problem and identify various types of ecological sources by integrating multi-sourced data, which was used to address the issue of original information loss caused by overlay analysis and homogenization of eco-sources. Building upon these insights, the study utilizes the minimum cumulative resistance model, gravity model, and other theories to extract eco-corridors and nodes, thereby constructing an ESP (20 ecological sources, 30 ecological corridors, and 61 ecological nodes) for PLEUA. An optimized pattern of “one ring, two corridors, two zones, and multiple cores” is proposed in this study and provides policy recommendations for regional land development optimization and environmental management enhancement. This configuration serves as a crucial reference for achieving regional spatial optimization and sustainable development in the PLEUA. Furthermore, this study provides insights and ideas for other cities undergoing rapid urbanization to coordinate the interactions between human activities and the ecological security of natural resources during the process of urban expansion, promoting a healthy and sustainable urban expansion process.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".