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Title: Identifying outliers and assessing the accuracy of amino acid racemization measurements for geochronology: I. age calibration curves
Authors: Kosnik, MA
Kaufman, DS
Hua, Q
Keywords: Amino acids
Age estimation
Carbon 14
Issue Date: Nov-2008
Publisher: Elsevier
Citation: Kosnik, M. A., Kaufman, D. S., & Hua, Q. (2008). Identifying outliers and assessing the accuracy of amino acid racemization measurements for geochronology: I. age calibration curves. Quaternary Geochronology, 3(4), 308-327. doi:10.1016/j.quageo.2008.04.002
Abstract: Numerical ages derived from amino acid racemization (AAR) geochronology are typically based on calibration curves that relate the extent of AAR to the age of independently dated specimens. Here, we compare options for developing calibration curves and quantifying age uncertainties using AAR data from 481 late Holocene shells, and AMS 14C analyses of 36 shells of four molluscan taxa (Ethalia, Natica, Tellina, and Turbo) collected from shallow sediment cores from a back-reef lagoon of the central Great Barrier Reef. The four taxa differed substantially in the quality of their geochronogical results. Explicitly including data from specimens alive at the time of collection improves calibration curves, but weighting numerical ages based on their uncertainty has no effect. Calibration curve statistics do not adequately assess calibration uncertainty. The relation between ages inferred from different amino acids is recommended for identifying aberrant specimens and quantifying the uncertainty of inferred ages. For this study, the AAR ages based on two amino acids (aspartic acid and glutamic acid) exceed 200 yr or 20% of their mean inferred age in 15% of the specimens. Once these were removed, the mean age error (1σ) for individual specimens based on two amino acids analyzed in duplicate subsamples ranged from 53 to 142 yr for Tellina and Turbo, respectively, or about a 30% age error for these relatively young shells. This compares favorably with analytical errors estimated at 50 yr or 5%. The presence of notable outliers undetectable using data from single amino acids emphasizes the importance of analyzing multiple amino acids. © 2008, Elsevier Ltd.
Gov't Doc #: 1421
ISSN: 1871-1014
Appears in Collections:Journal Articles

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