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Media Contacts
A new tool from Oak Ridge National Laboratory can help planners, emergency responders and scientists visualize how flood waters will spread for any scenario and terrain.
Oak Ridge National Laboratory researchers have developed a machine learning model that could help predict the impact pandemics such as COVID-19 have on fuel demand in the United States.
Oak Ridge National Laboratory scientists evaluating northern peatland responses to environmental change recorded extraordinary fine-root growth with increasing temperatures, indicating that this previously hidden belowground mechanism may play an important role in how carbon-rich peatlands respond to warming.
Researchers at Oak Ridge National Laboratory developed a method that uses machine learning to predict seasonal fire risk in Africa, where half of the world’s wildfire-related carbon emissions originate.
Ada Sedova’s journey to Oak Ridge National Laboratory has taken her on the path from pre-med studies in college to an accelerated graduate career in mathematics and biophysics and now to the intersection of computational science and biology
An international team of scientists found that rules governing plant growth hold true even at the edges of the world in the Arctic tundra.
While some of her earth system modeling colleagues at ORNL face challenges such as processor allocation or debugging code, Verity Salmon prepares for mosquito swarms and the possibility of grizzly bears.
A team of scientists found that critical interactions between microbes and peat moss break down under warming temperatures, impacting moss health and ultimately carbon stored in soil.
Elizabeth Herndon believes in going the distance whether she is preparing to compete in the 2020 Olympic marathon trials or examining how metals move through the environment as a geochemist at the Department of Energy’s Oak Ridge National Laboratory.
A detailed study by Oak Ridge National Laboratory estimated how much more—or less—energy United States residents might consume by 2050 relative to predicted shifts in seasonal weather patterns