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AI tool pools microplastics data
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This news piece describes an AI tool developed to harmonize the fragmented terminology used across thousands of microplastics studies, where researchers currently use thousands of different categories for materials, shapes, and size ranges. The tool, developed by Win Cowger and collaborators at the Moore Institute for Plastic Pollution Research, enables datasets from disparate studies to be combined for broader analysis.
As microplastics have multiplied , so have the studies quantifying them. And across all those studies, plastics researchers use thousands of different categories for materials, shapes, and size ranges, which means those mounting data can’t be easily combined to show a larger picture. In terms of data compatibility, “it’s been the Wild West for a long time,” says Win Cowger, research director at Moore Institute for Plastic Pollution Research . “We need a tool to wrangle everything together.” Cowger joined forces with environmental science graduate student Hannah Hapich and hydrologist Andrew Gray at the University of California, Riverside , to create such a tool ( Environ. Sci. Technol. 2024, DOI: 10.1021/acs.est.4c02406 ). Hapich saw how the growth in natural language processing (NLP) technology, computing power, and open-source artificial intelligence software could automate data harmonization in a way that wasn’t possible even a year ago. The team’s AI-powered model sorts material
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