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WxCrop is presented, the first agent that judges agricultural hazards from physically interpretable weather indices examined by causal analysis, and the first that combines weather and agricultural information to predict crop loss reliably for past and forecast dates.
An agentic search framework for geoscience data discovery that takes a natural-language research query and returns matching datasets and tools and demonstrates that, in the era of large language models, the latent value of knowledge graphs can be substantially amplified through agentic search.
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