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The Arctic climate system is in great distress, warming faster than the rest of the world and transforming more rapidly than previously anticipated. Sustained and harmonized multidisciplinary observations at key locations are needed to fill knowledge gaps and evaluate the ongoing climate change impacts on the complex Arctic marine system. For more than a decade, the Distributed Biological Observatory (DBO) has functioned as a “detection array” for ecosystem changes and trends in the Pacific sector of the Arctic Ocean. This long-term collaborative initiative builds on active involvement of scientists conducting in situ observations within marine disciplines to systematically document how the arctic marine ecosystem is transforming with environmental change. The DBO concept is currently being expanded into other sectors of the Arctic, including Davis Strait and Baffin Bay, the Atlantic Arctic gateway area, and the East Siberian Sea. Through increased collaboration and joint practices, findings from these regional areas can leverage to pan-Arctic perspectives and improve our understanding of the entire Arctic Ocean. Common practices are now being developed, including key phenomena and relevant indicators to study. Also, we strive towards harmonized routines for sampling, analysis and data sharing to increase the comparability across both disciplines and regions, and to improve the usability of our in-situ observations also for the modelling and remote sensing scopes. An ambition is, moreover, to expand from today's predominantly open-sea coverage towards coastal regions, to the benefit of both local communities and researchers. The process of establishing a pan-Arctic DBO network is to a large part facilitated by the EU Horizon project Arctic PASSION (2022-2025). Here, we present the latest developments and shared priorities, as well as our vision of how to incorporate our efforts into other parallel processes aiming to strengthen the pan-Arctic observing system towards, during and beyond the upcoming IPY.

The Arctic climate system is in great distress, warming faster than the rest of the world and transforming more rapidly than previously anticipated. Sustained and harmonized multidisciplinary observations at key locations are needed to fill knowledge gaps and evaluate the ongoing climate change impacts on the complex Arctic marine system. For more than a decade, the Distributed Biological Observatory (DBO) has functioned as a “detection array” for ecosystem changes and trends in the Pacific sector of the Arctic Ocean. This long-term collaborative initiative builds on active involvement of scientists conducting in situ observations within marine disciplines to systematically document how the arctic marine ecosystem is transforming with environmental change. The DBO concept is currently being expanded into other sectors of the Arctic, including Davis Strait and Baffin Bay, the Atlantic Arctic gateway area, and the East Siberian Sea. Through increased collaboration and joint practices, findings from these regional areas can leverage to pan-Arctic perspectives and improve our understanding of the entire Arctic Ocean. Common practices are now being developed, including key phenomena and relevant indicators to study. Also, we strive towards harmonized routines for sampling, analysis and data sharing to increase the comparability across both disciplines and regions, and to improve the usability of our in-situ observations also for the modelling and remote sensing scopes. An ambition is, moreover, to expand from today's predominantly open-sea coverage towards coastal regions, to the benefit of both local communities and researchers. The process of establishing a pan-Arctic DBO network is to a large part facilitated by the EU Horizon project Arctic PASSION (2022-2025). Here, we present the latest developments and shared priorities, as well as our vision of how to incorporate our efforts into other parallel processes aiming to strengthen the pan-Arctic observing system towards, during and beyond the upcoming IPY.

This study develops a multidimensional framework to assess cumulative exposure to climaterelated risks across Europe, integrating health, energy, transport, and socioeconomic conditions. By mapping risk distribution across regions and measuring dependence, we capture the interconnectedness of exposures and identify key socioeconomic drivers. Our findings reveal a substantial variation in risk distribution, with no clear geographical patterns. Unsurprisingly, household income emerges as the strongest determinant of exposure. We extend this analysis by projecting cumulative exposure to 2050, applying climate scenarios. The results suggest gradual rather than sharp change in exposure over time, with some areas exhibiting sharp rises; however, average risks are expected to rise across the entire continent.

This study develops a multidimensional framework to assess cumulative exposure to climaterelated risks across Europe, integrating health, energy, transport, and socioeconomic conditions. By mapping risk distribution across regions and measuring dependence, we capture the interconnectedness of exposures and identify key socioeconomic drivers. Our findings reveal a substantial variation in risk distribution, with no clear geographical patterns. Unsurprisingly, household income emerges as the strongest determinant of exposure. We extend this analysis by projecting cumulative exposure to 2050, applying climate scenarios. The results suggest gradual rather than sharp change in exposure over time, with some areas exhibiting sharp rises; however, average risks are expected to rise across the entire continent.

The integration of large language models (LLMs) in domain-specific applications has been limited due to high computational costs, and the need for expensive and challenging training datasets. This thesis explores Retrieval-Augmented Generation (RAG) and Graph-RAG pipelines to enhance question-answering precision in geothermal energy, addressing these challenges while optimizing computational efficiency. In this thesis, a domain-specific RAG pipeline for geothermal energy is firstly developed by fine-tuning an open-source classifier and embedding model to improve information retrieval. The RAG pipeline uses an open-source LLM to address concerns over proprietary models. The classifier effectively filters relevant geothermal data, increasing domain focus, while the optimized embedding model enhances retrieval accuracy. The results demonstrate that applying RAG improves question-answering accuracy from 55.5% using an untrained embedding model to 72.5% with a fine-tuned embedding model. Additionally, the fine-tuned classifier achieved over 99% precision in classifying text based on context. Meanwhile, the study highlights the environmental impact of increased computational demands, emphasizing the trade-offs between retrieval accuracy and CO2 emissions. A Graph-RAG approach, which enhances RAG by integrating structured relationships between entities, is then employed to improv contextual understanding. Unlike traditional RAG, which relies solely on similarity-based retrieval, Graph-RAG incorporates concept relationships to refine responses. The study evaluates Graph-RAG’s performance in geothermal energy question-answering tasks and demonstrates a 13% improvement in precision compared to RAG, particularly when retrieving fewer nodes and relationships. Moreover, Graph-RAG reduces computational costs by achieving similar accuracy to RAG while using 35% fewer input tokens. This ii improvement comes from Graph-RAG’s ability to leverage nodes and their relationships to better understand the concept. The study further reveals that Graph-RAG is more resilient against misleading statements by cross-referencing nodes and relationships between concepts. This research contributes to the advancement of AI-driven information retrieval in energy engineering by demonstrating the effectiveness of RAG and Graph-RAG pipelines. The findings highlight the benefits of structured entity relationships in improving precision, reducing computational costs, and optimizing knowledge retrieval. The thesis concludes that Graph-RAG offers a more efficient and reliable approach for domain-specific question answering, paving the way for future applications in geothermal energy and beyond.

The integration of large language models (LLMs) in domain-specific applications has been limited due to high computational costs, and the need for expensive and challenging training datasets. This thesis explores Retrieval-Augmented Generation (RAG) and Graph-RAG pipelines to enhance question-answering precision in geothermal energy, addressing these challenges while optimizing computational efficiency. In this thesis, a domain-specific RAG pipeline for geothermal energy is firstly developed by fine-tuning an open-source classifier and embedding model to improve information retrieval. The RAG pipeline uses an open-source LLM to address concerns over proprietary models. The classifier effectively filters relevant geothermal data, increasing domain focus, while the optimized embedding model enhances retrieval accuracy. The results demonstrate that applying RAG improves question-answering accuracy from 55.5% using an untrained embedding model to 72.5% with a fine-tuned embedding model. Additionally, the fine-tuned classifier achieved over 99% precision in classifying text based on context. Meanwhile, the study highlights the environmental impact of increased computational demands, emphasizing the trade-offs between retrieval accuracy and CO2 emissions. A Graph-RAG approach, which enhances RAG by integrating structured relationships between entities, is then employed to improv contextual understanding. Unlike traditional RAG, which relies solely on similarity-based retrieval, Graph-RAG incorporates concept relationships to refine responses. The study evaluates Graph-RAG’s performance in geothermal energy question-answering tasks and demonstrates a 13% improvement in precision compared to RAG, particularly when retrieving fewer nodes and relationships. Moreover, Graph-RAG reduces computational costs by achieving similar accuracy to RAG while using 35% fewer input tokens. This ii improvement comes from Graph-RAG’s ability to leverage nodes and their relationships to better understand the concept. The study further reveals that Graph-RAG is more resilient against misleading statements by cross-referencing nodes and relationships between concepts. This research contributes to the advancement of AI-driven information retrieval in energy engineering by demonstrating the effectiveness of RAG and Graph-RAG pipelines. The findings highlight the benefits of structured entity relationships in improving precision, reducing computational costs, and optimizing knowledge retrieval. The thesis concludes that Graph-RAG offers a more efficient and reliable approach for domain-specific question answering, paving the way for future applications in geothermal energy and beyond.