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  "notes": "Based on the annual report, here are the significant 
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  "notes": "Based on the annual report, here are the significant 
            
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results for the Sites & Stories Application and AI approaches that 
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results for the Sites & Stories Application and AI approaches that 
            
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Suzanne Pierce and Texas Advanced Computing Center contributed 
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Suzanne Pierce and Texas Advanced Computing Center contributed 
            
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to:\r\n\r\n## 1. Deployed Scalable AI-Enabled Analytical 
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to:\r\n\r\n## 1. Deployed Scalable AI-Enabled Analytical 
            
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Infrastructure\r\n\r\nThe Sites & Stories (S&S) application advanced 
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Infrastructure\r\n\r\nThe Sites & Stories (S&S) application advanced 
            
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from testing to full deployment as a reusable tool on Texas Advanced 
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from testing to full deployment as a reusable tool on Texas Advanced 
            
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Computing Center Resources. The system now provides cross-scale 
22
Computing Center Resources. The system now provides cross-scale 
            
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applications of Natural Language Processing, topic modeling, and Large 
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applications of Natural Language Processing, topic modeling, and Large 
            
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Language Model capabilities through a science gateway data portal that 
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Language Model capabilities through a science gateway data portal that 
            
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enables analyses at scale with HPC and GPU compute. LLM tools were 
25
enables analyses at scale with HPC and GPU compute. LLM tools were 
            
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successfully incorporated into the S&S application suite with open and 
26
successfully incorporated into the S&S application suite with open and 
            
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reproducible access as of June 2024.\r\n\r\n## 2. Validated 
27
reproducible access as of June 2024.\r\n\r\n## 2. Validated 
            
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Field-Ready Data Collection and Analysis Capabilities  \r\n\r\nThe 
28
Field-Ready Data Collection and Analysis Capabilities  \r\n\r\nThe 
            
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team completed 25 performance field tests in Nunapitchuk to evaluate 
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team completed 25 performance field tests in Nunapitchuk to evaluate 
            
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the Sites & Stories data collection and ingest services using 
30
the Sites & Stories data collection and ingest services using 
            
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variations in hardware, software, and data catalog capabilities. These 
31
variations in hardware, software, and data catalog capabilities. These 
            
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tests demonstrated the system's ability to function in remote Arctic 
32
tests demonstrated the system's ability to function in remote Arctic 
            
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environments while supporting both interview data collection and drone 
33
environments while supporting both interview data collection and drone 
            
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terrain data processing. The application successfully automated 
34
terrain data processing. The application successfully automated 
            
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analytical approaches with AI to connect qualitative and quantitative 
35
analytical approaches with AI to connect qualitative and quantitative 
            
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knowledge in well-defined workflows.\r\n\r\n## 3. Advanced Semantic 
36
knowledge in well-defined workflows.\r\n\r\n## 3. Advanced Semantic 
            
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Linking and Automated Knowledge Integration\r\n\r\nThe S&S application 
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Linking and Automated Knowledge Integration\r\n\r\nThe S&S application 
            
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was enhanced with semantic linking mechanisms to automate 
38
was enhanced with semantic linking mechanisms to automate 
            
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identification of relevant data collections and models for locally 
39
identification of relevant data collections and models for locally 
            
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identified stakeholder problems. The system now enables semantic 
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identified stakeholder problems. The system now enables semantic 
            
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linking of stakeholder interview information with digital terrain 
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linking of stakeholder interview information with digital terrain 
            
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models, creating culturally-informed technical resources. This 
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models, creating culturally-informed technical resources. This 
            
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advancement allows for automated connection of community knowledge 
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advancement allows for automated connection of community knowledge 
            
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with scientific models and observational data.\r\n\r\n## 4. 
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with scientific models and observational data.\r\n\r\n## 4. 
            
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Demonstrated Transferability and Scalability Across 
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Demonstrated Transferability and Scalability Across 
            
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Contexts\r\n\r\nThe capabilities were successfully replicated and 
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Contexts\r\n\r\nThe capabilities were successfully replicated and 
            
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tested for transferability in the Rio Grande Valley of Texas, proving 
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tested for transferability in the Rio Grande Valley of Texas, proving 
            
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the system's applicability beyond Arctic contexts. The S&S application 
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the system's applicability beyond Arctic contexts. The S&S application 
            
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was deployed for broad use by researchers with training provided to 
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was deployed for broad use by researchers with training provided to 
            
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large research teams and community partners. The system is now fully 
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large research teams and community partners. The system is now fully 
            
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scalable, reusable, and easily modified across different settings and 
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scalable, reusable, and easily modified across different settings and 
            
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use cases.\r\n\r\n## 5. Established Reusable Computational Cookbook 
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use cases.\r\n\r\n## 5. Established Reusable Computational Cookbook 
            
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Framework  \r\n\r\nAI-enabled analytical workflows are now available 
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Framework  \r\n\r\nAI-enabled analytical workflows are now available 
            
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across multiple scales, from individual narrative analysis through 
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across multiple scales, from individual narrative analysis through 
            
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Natural Language Processing computational cookbooks to topical 
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Natural Language Processing computational cookbooks to topical 
            
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analysis using BERTtopic cookbook, plus open-ended analysis tools 
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analysis using BERTtopic cookbook, plus open-ended analysis tools 
            
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using four Large Language Foundation Model options on TACC systems. 
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using four Large Language Foundation Model options on TACC systems. 
            
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These workflows provide mechanisms for linking lived experience 
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These workflows provide mechanisms for linking lived experience 
            
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information with observational data and physics-based models in 
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information with observational data and physics-based models in 
            
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convergent applications, with the full code base and reusable products 
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convergent applications, with the full code base and reusable products 
            
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available via Science Gateway applications and GitHub.",
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