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• W3094439952 abstract "Significance We demonstrate that combining optical superresolution imaging with deep learning classification methods increases the speed and accuracy of assessing the biological affinities of fossil pollen taxa. We show that it is possible to taxonomically separate pollen grains that appear morphologically similar under standard light microscopy based on nanoscale variation in pollen shape, texture, and wall structure. Using a single pollen morphospecies, Striatopollis catatumbus , we show that nanoscale morphological variation within the fossil taxon coincides with paleobiogeographic distributions. This new approach improves the taxonomic resolution of fossil pollen identifications and greatly enhances the use of pollen data in ecological and evolutionary research." @default.
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• W3094439952 date "2020-10-23" @default.
• W3094439952 modified "2023-10-06" @default.
• W3094439952 title "Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy" @default.
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• W3094439952 doi "https://doi.org/10.1073/pnas.2007324117" @default.
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