LUCAS-MEGA fuses 68 heterogeneous soil datasets into a 70k-sample multimodal collection and demonstrates its value by pretraining a tabular transformer whose representations align with established soil processes.
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UNVERDICTED 2representative citing papers
A fleet of sensor-specialized 22M-parameter JEPA models routed by an LLM improves LLM-as-judge scores on hydrologic questions over AlphaEarth alone with Cohen's d of 1.10.
citing papers explorer
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LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems
LUCAS-MEGA fuses 68 heterogeneous soil datasets into a 70k-sample multimodal collection and demonstrates its value by pretraining a tabular transformer whose representations align with established soil processes.
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Mini-JEPA Foundation Model Fleet Enables Agentic Hydrologic Intelligence
A fleet of sensor-specialized 22M-parameter JEPA models routed by an LLM improves LLM-as-judge scores on hydrologic questions over AlphaEarth alone with Cohen's d of 1.10.