OceanAI: A Conversational Platform for Accurate, Transparent, Near-Real-Time Oceanographic Insights
November 02, 2025 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Bowen Chen, Jayesh Gajbhar, Gregory Dusek, Rob Redmon, Patrick Hogan, Paul Liu, DelWayne Bohnenstiehl, Dongkuan Xu, Ruoying He
arXiv ID
2511.01019
Category
cs.CL: Computation & Language
Cross-listed
cs.AI,
cs.CE,
cs.LG,
physics.ao-ph
Citations
0
Venue
arXiv.org
Last Checked
6 months ago
Abstract
Artificial intelligence is transforming the sciences, yet general conversational AI systems often generate unverified "hallucinations" undermining scientific rigor. We present OceanAI, a conversational platform that integrates the natural-language fluency of open-source large language models (LLMs) with real-time, parameterized access to authoritative oceanographic data streams hosted by the National Oceanic and Atmospheric Administration (NOAA). Each query such as "What was Boston Harbor's highest water level in 2024?" triggers real-time API calls that identify, parse, and synthesize relevant datasets into reproducible natural-language responses and data visualizations. In a blind comparison with three widely used AI chat-interface products, only OceanAI produced NOAA-sourced values with original data references; others either declined to answer or provided unsupported results. Designed for extensibility, OceanAI connects to multiple NOAA data products and variables, supporting applications in marine hazard forecasting, ecosystem assessment, and water-quality monitoring. By grounding outputs and verifiable observations, OceanAI advances transparency, reproducibility, and trust, offering a scalable framework for AI-enabled decision support within the oceans. A public demonstration is available at https://oceanai.ai4ocean.xyz.
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