P
Pieter Vermeesch
Researcher at University College London
Publications - 142
Citations - 9558
Pieter Vermeesch is an academic researcher from University College London. The author has contributed to research in topics: Provenance & Zircon. The author has an hindex of 36, co-authored 126 publications receiving 6709 citations. Previous affiliations of Pieter Vermeesch include ETH Zurich & Stanford University.
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IsoplotR: A free and open toolbox for geochronology
TL;DR: The basic principles of radiometric geochronology as implemented in a new software package called IsoplotR, which was designed to be free, flexible and future-proof, are reviewed.
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On the visualisation of detrital age distributions
TL;DR: In this paper, the authors proposed Kernel Density Estimation (KDE), a more robust alternative to the Probability Density Plot (PDP), which also involves summing a set of Gaussian distributions, but does not explicitly take into account the analytical uncertainties.
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How many grains are needed for a provenance study
TL;DR: In this paper, the smallest number of grains in a sample that must be dated to achieve a required level of statistical adequacy is calculated, i.e., if no fraction of the population comprising more than 0.05 of the total is missed at the 95% confidence level, at least 117 grains should be dated.
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Community‐Derived Standards for LA‐ICP‐MS U‐(Th‐)Pb Geochronology – Uncertainty Propagation, Age Interpretation and Data Reporting
Matthew S.A. Horstwood,Jan Košler,George E. Gehrels,Simon E. Jackson,Noah McLean,Chad Paton,Norman J. Pearson,Keith N. Sircombe,Paul J. Sylvester,Pieter Vermeesch,James F. Bowring,Daniel J. Condon,Blair Schoene +12 more
TL;DR: The LA-ICP-MS U-(Th-)Pb geochronology international community has defined new standards for the determination of U-(th)-Pb ages as discussed by the authors.
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Multi-sample comparison of detrital age distributions
TL;DR: MDS is a robust and flexible superset of PCA which makes fewer assumptions about the data, and it is shown that the statistical effect size of the Kolmogorov–Smirnov test is a viable dissimilarity measure.