arXiv:2605.29966v1 Announce Type: new Abstract: Marine lead (Pb) and its isotopes are critical tracers for ocean circulation and anthropogenic pollution, yet in-situ observations remain costly and sparse. While vast historical records exist, they lie buried within the unstructured content of academic papers, creating "data silos" inaccessible to comprehensive analysis. Manual extraction is unscalable, while general-purpose Large Language Models (LLMs) lack the necessary domain-specific knowledge, leading to hallucinations and scientifically invalid outputs. To address this, we introduce an exp
The proliferation of LLMs creates both opportunities and challenges for extracting structured data from vast, unstructured scientific repositories, making expert-guided solutions essential. The increasing recognition of 'data silos' in critical scientific domains like marine science drives the need for advanced data integration tools.
This development addresses a critical bottleneck in scientific research by enabling the scalable extraction of essential environmental data, which was previously inaccessible for comprehensive analysis. It highlights the growing specialization of AI applications to overcome limitations of general-purpose models in specific, complex domains.
The ability to efficiently extract and integrate previously inaccessible scientific data from academic papers transforms our capacity for large-scale environmental analysis and modeling. It shifts the paradigm from manual, unscalable data collection to automated, AI-driven extraction guided by domain expertise.
- · Marine scientists
- · Environmental research institutions
- · AI agent developers
- · Data integration platforms
- · Traditional data extraction services
- · General-purpose LLMs without domain specialization
- · Research groups reliant on manual data curation
Domain-specific AI agents become critical tools for unlocking structured data from scientific literature across various fields.
Improved data availability leads to more robust environmental models and better-informed policy decisions regarding pollution and ocean health.
The success of expert-guided LLM agents encourages their development in other fields facing 'data silo' challenges, accelerating scientific discovery and data synthesis across disciplines.
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Read at arXiv cs.AI