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• W1972374522 abstract "Pollen is among the most ubiquitous of terrestrial fossils, preserving an extended record of vegetation change. However, this temporal continuity comes with a taxonomic tradeoff. Analytical methods that improve the taxonomic precision of pollen identifications would expand the research questions that could be addressed by pollen, in fields such as paleoecology, paleoclimatology, biostratigraphy, melissopalynology, and forensics. We developed a supervised, layered, instance-based machine-learning classification system that uses leave-one-out bias optimization and discriminates among small variations in pollen shape, size, and texture. We tested our system on black and white spruce, two paleoclimatically significant taxa in the North American Quaternary. We achieved > 93% grain-to-grain classification accuracies in a series of experiments with both fossil and reference material. More significantly, when applied to Quaternary samples, the learning system was able to replicate the count proportions of a human expert (R2 = 0.78, P = 0.007), with one key difference – the machine achieved these ratios by including larger numbers of grains with low-confidence identifications. Our results demonstrate the capability of machine-learning systems to solve the most challenging palynological classification problem, the discrimination of congeneric species, extending the capabilities of the pollen analyst and improving the taxonomic resolution of the palynological record." @default.
• W1972374522 created "2016-06-24" @default.
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• W1972374522 date "2012-09-03" @default.
• W1972374522 modified "2023-10-18" @default.
• W1972374522 title "Classifying black and white spruce pollen using layered machine learning" @default.
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• W1972374522 doi "https://doi.org/10.1111/j.1469-8137.2012.04291.x" @default.
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