Academic Journal
The Influence of Database Characteristics on the Internal Consistency of Predictive Models of Trace Element Partitioning for Clinopyroxene, Garnet, and Amphibole.
| Title: | The Influence of Database Characteristics on the Internal Consistency of Predictive Models of Trace Element Partitioning for Clinopyroxene, Garnet, and Amphibole. |
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| Authors: | Cung, Erica W., Ustunisik, Gokce K., Wolf, Aaron S., Nielsen, Roger L. |
| Source: | Geochemistry, Geophysics, Geosystems: G3; Jun2023, Vol. 24 Issue 6, p1-31, 31p |
| Abstract: | Understanding the processes that produce the major and trace element signature of igneous materials requires quantitative models of the behavior of trace elements under the full range of natural conditions. Such predictive models are based on the results of laboratory experiments used to calibrate expressions via regression analysis. The predictive accuracy of those expressions depends on the number of experiments where a specific element was measured and the analytical precision/accuracy of the measurements, together with models that accommodate the known dependencies. A factor that has rarely been considered in such models is the "coverage" with respect to the range of composition, pressure, and temperature in the experimental data. The goal of this research is to evaluate how partition coefficients (Di) for clinopyroxene, amphibole, and garnet correlate with a variety of intensive and compositional variables for minerals with different substitution mechanisms. Our results show that the number of experimental determinations, even within a group of elements that behave systematically (e.g., REE), may vary by as much as a factor of five. Further, there are significant differences in the average composition, temperature, and pressure of the experimental database for each element. In addition, the combination of database differences and analytical precision for each element result in systematic differences in the magnitude of the controlling parameters. All of these factors impact the predictive power of the regressions on which we rely and can produce a bias in the predicted behavior that may be correlated with analytical error or average composition of the experiments. Key Points: There are significant differences in average composition, T, and P of the experimental database for each element in different phasesNumber of experimental determinations, even within a group of elements that behave systematically (e.g., REE) may vary by a factor of fiveData coverage differences impact the predictive power of the regressions on which we rely trace element partitioning models [ABSTRACT FROM AUTHOR] |
| Subject Terms: | DATABASES, PREDICTION models, AMPHIBOLES, GARNET, REGRESSION analysis, TRACE elements |
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| ISSN: | 15252027 |
| DOI: | 10.1029/2023GC010876 |
| Database: | Complementary Index |
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