# AppendixA_Intro

Appendix A. Stream sediment geochemistry summary statistics and percentile cutoffs.     Geochemical data of sediment samples represents one of the most comprehensive, evenly spaced, and high-density datasets for estimating mineral resource potential across Alaska.  Bedrock is obscured in many areas by unconsolidated sediment and vegetation.  Sediment data show patterns of element concentration that reflect rock types in their respective drainage basins and, thus, provide clues about the composition of rocks in areas of poor exposure and limited geologic mapping. We refer to this dataset as stream sediment geochemistry because, although some samples were collected from a variety of surface water bodies, more than 80% of the sediment samples in the AGDB2 were collected from streams.     Stream sediment geochemical data from the AGDB2, NURE, and DGGS datasets were combined into a single dataset from which the median, 75th, 91st, and 98th percentiles were calculated. The combined dataset produces the greatest statewide coverage of geochemical data, but analytical methods differed among samples. Furthermore, the combined datasets contain multiple detection limits for a single element, thus resulting in multiple data censoring limits. Censored data are those that fall outside the detection limit for a specific element analyzed by a particular method. Some elements have highly censored data. For these elements, summary statistics were strongly skewed by omitting the censored data or substituting values such as one-half of the lowest determination limit. For example, if the geochemical data for an element are 90% censored, then substitution of a value such as one-half of the lowest determination limit for the censored data will yield artificially low mean, median, and percentile values. Similarly, if the data are 90% censored, then omission of the censored data will leave only anomalously enriched data, and statistics calculated on that portion of the data will yield much greater mean, median, and percentile values. Therefore, two schemes were used to calculate statistical values for each element depending on the amount of censored data: substitution and Kaplan-Meier.  Substitution: For elements with 40% or less censored data, all censored values were replaced with a value of half the minimum non-censored value, or half of the minimum lower limit of determination, whichever was the lesser of the two.  A trailing of 11 was added to make those replaced values identifiable in the database. Summary statistics were then calculated by standard procedures. Kaplan-Meier Method : For elements with more than 40% censored data, we used the Kaplan-Meier method for estimating summary statistics. This method is a nonparametric statistical approach that is frequently used in calculating summary statistics for censored data (Helsel, 2012). The procedure used here is detailed in Helsel (2012) and was run using Minitab. The Kaplan-Meier method was useful in this study because it accommodates multiple censoring levels, such as those in the stream sediment geochemistry dataset. The method uses the uncensored data and the relative information contained within the censored data to estimate the summary statistics. Each unique uncensored value is placed in numeric order, and ranked such that the censored data are accounted for during the ranking  (Helsel, 2012). To rank the uncensored data, the censored values are placed at the determination limit for their respective method because the true analytic value of the sample cannot be above this value, and then the rank of the uncensored value jumps to account for number of intervening censored values. Percentiles are estimated without interpolation, and the mean is estimated by integrating the area under the Kaplan-Meier survival curve (Helsel, 2012). This appendix shows summary statistics for each element, which database(s) contained data for each element, and the statistical method by which percentile values were determined. These percentile cutoff values were used in the mineral potential scoring methods described in the main body of the report. Helsel, D.R., 2012, Statistics for censored environmental data using Minitab and R (2nd ed.): New York, John Wiley and Sons, 344 p. 

# Statistical values by element

# Metadata