M
Michael L. Grieneisen
Researcher at University of California, Davis
Publications - 41
Citations - 2134
Michael L. Grieneisen is an academic researcher from University of California, Davis. The author has contributed to research in topics: Population & Environmental science. The author has an hindex of 17, co-authored 34 publications receiving 1571 citations. Previous affiliations of Michael L. Grieneisen include Wenzhou Medical College & University of North Carolina at Chapel Hill.
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Botanical insecticide research: many publications, limited useful data.
TL;DR: Much of the scientific literature on botanical insecticides is of limited use in the progress toward commercialization or advancement of knowledge, given the resources expended.
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A Comprehensive Survey of Retracted Articles from the Scholarly Literature
TL;DR: The scope and characteristics of retracted articles across the full spectrum of scholarly disciplines were surveyed, and 15 prolific individuals accounted for more than half of all retractions due to alleged research misconduct, and strongly influenced all retraction characteristics.
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Recent advances in our knowledge of ecdysteroid biosynthesis in insects and crustaceans
TL;DR: Developmental correlations suggest that the sterol 7,8-dehydrogenase and terminal hydroxylases in Manduca prothoracic glands are not regulated by protharacicotropic hormone, in contrast to the utilization of 7- dehydrocholesterol in crustaceans, which is influenced by molt inhibiting hormone.
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Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm
Yu Zhan,Yuzhou Luo,Xunfei Deng,Huajin Chen,Michael L. Grieneisen,Xueyou Shen,Lizhong Zhu,Minghua Zhang +7 more
TL;DR: In this article, a novel machine learning algorithm, Geographically-Weighted Gradient Boosting Machine (GW-GBM), was developed by improving GBM through building spatial smoothing kernels to weigh the loss function.
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Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment.
TL;DR: This study is the first statistical modeling work of ambient O3 for China at the national level and shows comparable or higher predictive performance based on only a handful of readily-available variables at much lower computational cost.