New criteria for sustainable land use planning of metropolitan green infrastructures in the tropical Andes
Bibliographic record
Abstract
Context: Urbanization is rapidly increasing worldwide, with about 60% of the global population currently residing in cities and expected to reach 68% by 2050. In Latin America's tropical Andes region, managing these changes poses challenges, including biodiversity loss and vulnerability to climate change. Objectives: This study assesses urban growth and agricultural intensification impacts on the ecological functionality of metropolitan green infrastructures and their capacity to provide ecosystem services using a landscape sustainability and sociometabolic approach. Specifically, it aims to identify landscape configurations promoting socio-ecological sustainability amidst rapid urbanization. Methods: A landscape-metabolic model (IDC) was applied to evaluate the interactions between land use changes and ecosystem functions in the metropolitan region of Cali. Results: Agricultural intensification and industrialization, coupled with uncontrolled urban growth, have significantly transformed the landscape, posing threats to its sustainability. The prevailing biocultural landscapes hold a substantial potential to provide essential ecosystem services to the metropolis. The IDC offers an approach that utilizes a land cover map and agricultural production/metabolism data to calculate an indicator closely related to ecosystem services and multifunctionality. Conclusions: The IDC model stands out for efficiently capturing landscape dynamics, providing insights into landscape configuration and social metabolism without extensive resource requirements. This research highlights the importance of adopting a landscape-metabolic and green infrastructure framework to guide territorial policies in the tropical Andes and similar regions. It stresses the need for informed land use planning to address challenges and leverage opportunities presented by biocultural landscapes for regional sustainability amidst rapid urbanization and agricultural expansion.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".