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A new machine-learning method developed by researchers at the University of Minnesota, University of Pittsburgh, and U.S. Geological Survey will provide more accurate stream and river temperature predictions, even when little data is available. These temperature predictions are used to determine suitability of aquatic habitats, evaporation rates, greenhouse gas exchange, and efficiency of thermoelectric energy production.
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