Revolutionizing ecological security pattern with multi-source data and deep learning: An adaptive generation approach
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Résumé
• 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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle