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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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Displaying entries 106 through 107 of 107 found.

Earth Earns: An Open Participatory Earthropocene to Astropocene CoCreative Future

Ecosmo Sapiens > Viable Gaia

Hylander, Kristoffer, et al. Lessons from Ethiopian coffee landscapes for global conservation in a post-wild world.. Communications Biology. 7/714, 2024. This paper by Stockholm University, Addis Ababa University and Leuphana University, Germany environmentalists is a good example of worldwise endeavors to retrospectively apply a whole systems analysis to a biocultural subject area so as to gain a steady sustainability.

The reality for conservation of biodiversity is that all ecosystems are modified by humans in some way. In this paper we use a coffee landscape in Ethiopia as our lens to derive general lessons for a post-wild world. Considering a hierarchy of scales from genes to multi-species interactions and social-ecological system contexts, we focus on the genetic diversity of crop wild relatives, trade-offs between biodiversity and agricultural yields, pest and disease levels, land-use change and restoration, and how to work with stakeholders for sustainable development. The ubiquitous presence of our human-nature immersions needs creative solutions to foster biodiversity conservation across entire landscapes. (Abstract)

Ecosmo Sapiens > Viable Gaia

Wandeto, John and Birgitta Dresp-Langley. Explainable Self-Organizing Artificial Intelligence Captures Landscape Changes Correlated with Human Impact Data.. arXiv:2405.09547. Dedan Kimathi University of Technology, Kenya and CNRS, Strasbourg University computational AI scholars (search BDL) post a frontier example of how novel machine neural learning methods can serve and benefit land environmental policies in developing countries. In addition, we note still another instance of a self-making agencies even in algorithmic software

Novel methods of analysis are needed to help advance our understanding of the intricate interplay between landscape changes, population dynamics, and sustainable development. Self-organized machine learning has been highly successful in the analysis of visual data the human expert eye may not see. Thus, subtle but significant changes in images of trends in natural or urban landscapes may remain undetected. Capturing such evidence as early on can make critical information readily available to citizens, professionals and policymakers. Here, we use unsupervised Artificial Intelligence (AI) that exploits principles of self-organized biological visual learning for the analysis of imaging time series. This method is combined with the statistical analysis of demographic data to reveal human impacts. (Excerpt)

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