Trace Element Discrimination of Discrete Sources of Native Copper

14 Trace Element Discrimination of Discrete Sources of Native Copper G E O R G E RAPP, JR., and J A M E S A L L E R T

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University of Minnesota, Archaeometry Laboratory, Duluth ΜΝ 55812 EILER H E N R I C K S O N Carlton College, Department of Geology, Northfield ΜΝ 55057

Activation

analysis is used to establish trace element "fin

gerprints"

of geologic deposits of native copper. By using

the statistical

techniques

of discriminant

analysis and K

-means cluster analysis and the trace element in artifact

concentrations

copper, an assignment of probable

geographic

and(or) geologic source of the artifact raw material can be made.

T

H E U S E O F T R A C E E L E M E N T A N A L Y S I S to determine the provenance of archaeological materials has expanded rapidly i n the last decade. It

is now a well-established technique for the identification of obsidian source deposits (J), and is nearly as established for turquoise (2), steatite (3), and some ceramic materials (4). Native copper has received m u c h

less attention. F r i e d m a n et al. (5), Fields et al. (6), and Bowman et al. (7) used trace element analyses to determine the type of geological ore from which copper was extracted. However, only our efforts (8) and the work of G o a d and Noakes (9) have focused on collecting and analyzing native copper from all potential deposits of a given region to provide a data base for statistical comparison with artifact trace element compo sitions. Our

trace element data base now contains analyses of 586 samples

of native copper from deposits throughout the world. However, the sample sources are skewed toward the northern United States, especially the Lake Superior region. Trace elements can be considered as those normally found in concentrations below 100 p p m (i.e., below the 0.01% normally used as the lower limit of standard rock and mineral analyses). Trace elements do not play a major part in the physicochemical reactions that take place i n the formation of geologic deposits. T h e y are either concentrated in or dispersed throughout rock, mineral, and ore deposits 0065-2393/84/0205-0273$06.25/0 © 1984 American Chemical Society

Lambert; Archaeological Chemistry—III Advances in Chemistry; American Chemical Society: Washington, DC, 1984.

274

ARCHAEOLOGICAL CHEMISTRY


by virtue of their residence in the bulk chemical system and involvement in certain phases of geochemical alteration. It would be highly unlikely that two unsmelted (or otherwise chemically altered) artifacts would have coincident trace element concentrations of eight or more geochemically independent elements unless they were made of material from the same rock or ore body. Background information on "fingerprinting" native copper published in Reference 8 will not be repeated here. O u r first neutron activation analyses were done in the m i d 1960s at Argonne National Laboratory. Most of the analyses reported here were done at the nuclear reactor facility at the University of Wisconsin, Madison (Richard Cashwell, d i rector). A thermal neutron flux of about 10 neutrons/cm /s was used. T h e gamma radiation was counted using a Ge(Li) detector and the spectra were analyzed by Wisconsin's N A A C A L C program. Initially we used a variety of standards. F r o m 1973 through 1980, gold was the only internal standard used. Since 1981 a new standard, the Canadian Reference Soil Sample, has been used. 12

2

O u r initial analyses sought only eight elements: silver, cobalt, chrom i u m , iron, mercury, antimony, scandium, and selenium. Beginning in 1973 arsenic, gold, cadmium, cerium, cesium, europium, hafnium, i n d i u m , iridium, lutecium, nickel, ruthenium, tin, tantalum, tellurium, thorium, tungsten, ytterbium, and zinc were added, and in 1981 molybdenum was added. T h e number and variety of elements examined were largely a function of the capabilities of the analytical instrumentation used and the evolution of techniques over the course of time. Changes in the standards, the gamma wavelengths used for given elements, the elements sought, and the computer programs used to measure gamma radiation intensities have made the data unhomogeneous. However, it is our contention that the inherent heterogeneity of the raw materials and the lack of any ancient technology to remove impurities probably combine to make measured differences in trace element concentrations of less than one-third of an order of magnitude (i.e., concentration differences from 0.3 to 3 times) insignificant in provenance studies of prehistoric copper and bronze artifacts. The Archaeometry Laboratory trace element data base contains 1980 analyses, all undertaken by the laboratory with assistance from personnel at the reactor facilities at the University of Wisconsin, Madison, or at Argonne National Laboratory. T h e distribution of analyses by material analyzed is presented in the box on the next page.

Statistical Analyses A wide variety of statistical techniques has been used in archaeometry to assign artifacts to sources or to similarity groups based on trace chem-



14.

RAPP ET AL.

Native Copper Source Discrimination

275

ical data. W e have used two techniques primarily, a discriminant function and a K-means cluster analysis. Discriminant analysis can be used when the investigator has a priori knowledge of the specific group to which an unknown must belong. T h e discriminant function is then a decision rule that assigns the unknown to one group on the basis of a set of measurements. T h e coherence and uniqueness of each group can be tested by removing members from groups to check if the decision rule returns them to their (known) group. Incorrect or overlapping groups can be uncovered by such tests. Cluster analysis is a statistical technique for determining relationships in a large matrix of measurements. W e have found that the common agglomerative-hierarchical dendrograms expressing relationships among trace element patterns in copper deposits are not helpful in assigning unknowns (artifacts) to probable sources. However, by using cluster analysis as a simple form of correlation analysis, the results can be presented in a two-dimensional diagram where group separations and overlaps are easily seen. This method can be an important tool in provenance studies. K-means cluster analysis uses standard Euclidean distance as a measure of similarity between reference groups or between unknowns and the reference groups (in our case, copper deposits). T h e technique allows the operator to seek natural groups of any desired level of similarity. T h e Discriminant Function d * . In order to assign a copper artifact to the copper deposit of most probable origin, it is necessary to use statistical techniques compatible with the data. Several discriminant func-


276


tions were tested by Rapp et al. (S) in order to come up with an appro priate design. W e decided to use a simple univariate product function wherein the trace element concentrations in an artifact are compared to all trace element analyses from specified coherent geographical sources, anywhere in size from a single deposit (mine) to a large region, which form a population defining a trace element "fingerprint." F o r each spec ified geographic source, the trace element data are arranged in a twodimensional matrix with the analytical data for each chemical element recorded in five concentration intervals, I to I . T h e intervals are dif ferent for each chemical element and are chosen from an inspection of the data base to maximize the discriminating power of the function. T h e concentration intervals and the measured concentrations for 27 elements are given in the Appendix. 5


x

T h e product function d * compares the unknown (artifact) with each possible source fingerprint by

where η is the number of analyses and N is the number of analyses falling into the ith concentration interval of the jth element. T h e potential locality with the highest product value is the indicated source. The vi fj

ability of this simple linear discriminant function is totally dependent on a priori knowledge that any unknown is a member of one of the source sets. In other words, if not all potential sources are represented in the data base, then the d * will assign as the source that locality where the product value is maximum. N o part of d * is designed to reject a prove nance determination because the unknown is insufficiently similar to all given sources in trace element abundances. A fingerprint of any of the sources listed in the Appendix can be assembled from the information presented. T h e Appendix is organized such that others may compare results with our data. T h e concentration ranges i n parts per million for the elements are given in the Appendix. A six-element fingerprint for Snake River, M i n n . , would be as follows:

Ii I I I Is 2

3

4

Co

Te

Fe

Hg

Sb

W

4 0 0 15 0 19

10 0 7 2 0 19

3 0 0 8 8 19

4 0 0 15 0 19

5 0 1 12 1 19

9 1 9 0 0 19

This means, for example, that of the 19 analyzed specimens from Snake River, 4 of the cobalt trace element concentrations were in the


14.

RAPP ET AL.

277


range I and 15 were in the range encompassed by I . T h e other ranges x

4

contain no values. T h e reason for the bimodality in the Snake River abundances shown above is not known. F o r the six-element Snake River fingerprint

above, an unknown whose analysis placed cobalt in I , tel4

lurium in I , iron in I , mercury in I , antimony in I , and tungsten in 3

3

5

3

I would have a d * value for Snake River of 2

(s)(s) < x 0005

00 0 5

»-

00000000201


to be compared with d * values for all other potential sources (for examples, see Tables II, III, IV, and V). It should be noted that the sample calculation given in Reference 8 is incorrect. T h e test for the uniqueness of a fingerprint is accomplished by randomly removing specimen analyses one by one from a locality fingerprint, reconstituting the fingerprint without the test specimen values in the fingerprint, then using d * to assign the test specimen to a source. If all or nearly all are returned to the known source, then the fingerprint has a high level of uniqueness. Attempts to distinguish individual mines in northern Michigan provide the toughest test. T h e northern Michigan deposits are all of the same age and have similar geologic origins. H e n c e there is more overlap in fingerprint characteristics among these sources than between them and deposits in Alaska, Illinois, or Arizona. In a test of Isle Royale copper samples versus Kingston M i n e samples, 17 of 20 randomly selected samples were returned to the known source. Data for 10 sources in the United States are presented in the Appendix. O u r Kingston M i n e fingerprint is based on analyses of 159 specimens from throughout the modern mine. T h e trace element abundances vary with level in the mine. Prehistoric humans d i d not have the technology to engage in deep mining; therefore, the most appropriate fingerprint would be from samples taken only from the surface or at shallow depth. Statistically, 159 analyses from one source is a more than adequate number. However, the number of analyses from the C h a m p i o n mine is 9, which is statistically inadequate. F o r a 16-element fingerprint, 20 analyzed specimens is a marginal number; 25-40 are recommended. O u r work thus far indicates that the discriminant function seems to succeed if there are 20 or more analyzed samples in each locality fingerprint. T h e concentration intervals I through I for each element were chosen by inspecting the range of abundances using all 1027 analyses of native copper. By using the data shown in Table I, the intervals were chosen to distribute the concentrations as evenly as possible throughout the five I cells. Because many concentrations were below the detection limit, the lowest concentration interval (which includes "not detected") x

5


278


Table I. Concentration of 27 Elements from Analyses of Native Copper and Native Copper Artifacts /

Ii I I I i

2

3

4

5


Total J Ii I I 2

3

I4

Is

Total ί Ii I I

2

3

I4

I

5

Total

Ag

As

Au

Cd

Ce

Co

Cr

Cs

Eu

212 310 206 141 158

352 87 50 41 49

238 51 225 74 214

519 14 29 12 5

450 185 121 14 35

115 232 39 532 109

356 354 31 249 37

488 19 21 25 25

480 24 17 32 25

1,027

579

802

579

805

1,027

1,027

578

578

Fe

Hf

Hg

In

Ir

Lu

Ni

Ru

Sb

354 291 85 86 211

481 10 217 62 19

202 164 37 552 72

509 214 21 32 26

428 197 46 99 30

520 16 16 16 11

482 36 20 13 27

525 16 6 28 4

393 215 59 212 148

1,027

789

1,027

802

800

579

578

579

1,027

Sc

Se

Sn

Ta

Te

Th

W

Yb

Zn

341 247 155 163 120

445 248 211 89 34

518 7 11 17 26

528 4 22 18 7

479 3 67 26 4

496 13 21 48 0

334 142 29 33 41

520 24 20 14 0

422 26 73 20 38

1,026

1,027

579

579

579

578

579

578

579

NOTE: Data are given as parts per million.

tends to be more populous than the other intervals. T h e ability of d * to function as a discriminator depends on the choice of trace elements and intervals used. F o r specific problems, the effectiveness of d * can be maximized by refining the intervals and the choice of elements by using only the data from relevant localities. T o illustrate the range of trace element abundances throughout the intervals I

x

through I

combines the 586

5

for all native copper in the data base, Table I

analyses of native copper samples from deposits

throughout the world with the 441 analyses of native copper artifacts from North America. Restructuring Table II into locality fingerprints i n the form of the six-element fingerprint given above for Snake River illustrates which trace elements are important in establishing the degree of uniqueness of each locality. F o r example, high iron, zinc, europium, iridium, and nickel concentrations make the Illinois fingerprint unique in the data base.


14.

RAPP E T A L .


279

Table II. Summary of Unknown vs. Fingerprints (Specimen N o . , 34-182A; McKinstry, M i n n . ; bar artifact)

Area


Kingston, Mich. Champion, Mich. Centennial, Mich. Isle Royale, Mich. Snake River, Minn. Arizona Alaska

Discriminant Function 0.000006439059 0.000000000000 0.000000000000 0.000000028666 0.000105815251 0.000003576278 0.000000000111

NOTE: Fingerprint best fit, Snake River, Minn.

Table III. Summary of Unknown vs. Fingerprints (Specimen N o . , 34-182A)

Area Kingston, Mich. Champion, Mich. Centennial, Mich. Isle Royale, Mich. Snake River, Minn. Arizona Alaska Wisconsin Illinois Lake Michigan

Discriminant Function 0.000000007475 0.000000000000 0.000000000000 0.000000000000 0.000007125975 0.000000000000 0.000000000000 0.000000000000 0.000000000000 0.000000000000


Rapp et al. (S) presented five examples of the use of d * that indicated that artifacts from Petaga Point, McKinstry, and Snake River, M i n n . , all came from native copper outcrops or stream pebbles along Snake River. By using additional analyses and making some changes i n both the concentration ranges (I) and the elements used, the artifact (34-1-82A) from M c K i n s t r y that was assigned (8) to a Snake River source with the values shown i n Table II is now (Table III) assigned much more positively to a Snake River source. T h e major differences in d * values for localities listed in Tables II and III illustrate the sensitivity of d * to changes in I and i n the trace elements used. The Snake River fingerprint is not unique. Table I V shows a Snake River native copper specimen that barely returns to the Snake River group, being nearly assigned instead to Arizona or the Kingston, M i c h . , mine. Table V shows a typical example of the proper return by d * of a Kingston specimen to the Kingston group.



280

Table IV. Summary of Unknown vs. Fingerprints (Specimen N o . , 34-180T; Snake River Native Copper)


Area Kingston, Mich. Champion, Mich. Centennial, Mich. Isle Royale, Mich. Alaska Arizona Snake River, Minn. Wisconsin Illinois Lake Michigan



Table V . Summary of Unknown vs. Fingerprints (Specimen N o . , 34-2135DP; Kingston Mine Sample)

Area Kingston, Mich. Champion, Mich. Centennial, Mich. Isle Royale, Mich. Alaska Arizona Snake River, Minn. Wisconsin Illinois L a k e Michigan


NOTE: Fingerprint best fit, Kingston, Mich.

N i n e copper artifact samples from the Houska Point excavation site in northern Minnesota were recently analyzed and run against the data base using d * . Table V I presents the results. Houska Point is located i n northern Minnesota along the Canadian border. F r o m the analyses it appears that the earliest prehistoric inhabitants of the site may have secured their copper from deposits along the Snake River i n Central Minnesota, then later imported raw copper or finished products from sources on Isle Royale in Lake Superior (approximately the same distance away as Snake River) and from the extremely abundant surficial deposits in the U p p e r Peninsula of Michigan. T h e Kingston mine can be considered a surrogate for the U p p e r Michigan deposits. Although analyses of additional artifacts from Houska Point would be necessary before the


14.

RAPP ET AL.

281


Table V I . d * Assignments of Copper Artifacts, Houska Point Site, M i n n .

Excavation Level Level I Level II Level III

Level IV


Level V

Sample Number 83D 83E 83F 83G 83H 831 83J 83K 83L

Discriminant

Assignment

Isle Royale, Mich. Upper Michigan Upper Michigan Upper Michigan Isle Royale, Mich. Isle Royale, Mich. Isle Royale, Mich. Isle Royale, Mich. Snake River, Minn.

copper source pattern is firmly established, the potential of the d * func tion is evident. K - M e a n s Cluster Analysis.

K-means cluster analysis is an iterative

clustering technique with reallocation capability. [See Doran and Hodson (JO, pp. 180-85) for a general discussion of the application of K-means procedures to archaeological data sets.] T h e most important advantage of K-means clustering over the hierarchical-aggregative techniques more often used in N A A data analysis (JJ) is its ability to continuously review cluster membership and to reallocate members by an optimizing crite rion. In the K-means program used here (12), B M D P K M , the reallocation algorithm minimizes the Euclidean distance between the members of a given cluster and the cluster centroid. Initially all samples are members of a single cluster (i.e., Κ = 1); this cluster is subsequently subdivided until the final number of clusters specified by the user is attained. Sam ples are then iteratively reallocated to the cluster whose centroid is closest to them. T h e optimum K - n u m b e r for any clustering operation is deter m i n e d both quantitatively, by finding the K - n u m b e r with the smallest mean of average intracluster Euclidean distances, and qualitatively, by examining the scatter plot of the orthogonal projection of samples into the plane defined by the centroids of the three most populous clusters. K-means cluster analysis was found to be an effective way to dis criminate among a limited number of provenances. Figure 1 illustrates the clarity with which cluster divisions between two geographic prove nances (float copper specimens from various locations within Illinois and native copper specimens from the Snake River area in Minnesota) are displayed by the program output. Figure 1 is an orthogonal projection of the specimens making up each cluster into the plane passing through the center of the three most populous clusters. T h e ordinate and the abscissa represent positive and negative distances from the centroid of the initial cluster. Table VII lists the sample identifiers indicating the cluster assignment for each sample and the distance of the sample from



282

4.5 S n a k e River


^Illinois

-4.5

-2.5 Figure 1.

0

2.5

5.0

7.5

10.0 12.5

Projection into plane through the centers of clusters 2, I, and 3.

Table VII. Illinois (IL) and Snake River, M i n n . (SR), Cluster Composition Cluster 2 (21 Cases)

Cluster 1 (11 Cases) Case

Distance

Case

Distance

Case

Distance

IL-40A

2.4405

IL-45B

2.5053

SR-80K

4.7280

IL-41A

1.7755

IL-50A

2.5053

SR-80L

3.2164

IL-41B

1.4472

SR-80A

1.9143

SR-80M

1.8948

IL-44A

1.3196

SR-80B

1.9245

SR-80N

2.2841

IL-45A

1.1780

SR-80C

1.6576

SR-80O

2.7832

IL-46A

3.2341

SR-80D

1.9374

SR-80P

2.4516

IL-46B

2.8372

SR-80E

2.9347

SR-80Q

1.5592

IL-46C

0.9107

SR-80F

1.2840

SR-80R

1.9222

IL-46D

0.7805

SR-80G

5.8925

SR-80S

1.7175

IL^-48A

1.3032

SR-80I

3.4061

SR-80T

1.8935

IL-49B

0.7112

SR-80J

1.4870

the center of that cluster. Cluster 1 is composed exclusively of Illinois float coppers; cluster 2 contains all the Snake River coppers and two anomalous Illinois samples. Perfect clustering, such that no heterogeneous clusters would occur, was rarely achieved, yet overlap such as occurs i n cluster 2 above does not pose a major threat to interpretation or application of this form of cluster analysis. W h e r e sample overlap does become a problem, further clarification was often achieved by increasing the number of clusters. Table VIII


14.

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283

Table VIII. Five Midwestern Localities Cluster Composition Comparisons

Snake River

Illinois

Lake Wisconsin Michigan

Isle Royale

Five Clusters


Cluster Cluster Cluster Cluster Cluster

1 2 3 4 5

0 4 2 11 2

11 0 0 0 2

0 0 0 1 18

0 0 0 0 20

0 0 0 5 25

0 0 0 1 0 2 18 0 0

0 0 0 0 0 0 18 0 0

0 0 0 4 1 24 3 0 0

Nine Clusters Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster

1 2 3 4 5 6 7 8 9

0 4 2 4 0 2 2 0 5

10 0 0 0 0 0 2 1 0

NOTE: Modal values are in boldface type.

compares the cluster compositions of analyses made on float copper specimens from Illinois with specimens from the Lower Peninsula of M i c h igan, with specimens from Wisconsin, with native copper from sources on Isle Royale, and with specimens from Snake River in central M i n nesota. Ideally, five clusters should be enough to separate out these five distinct localities. However, specimen outliers (a function of total sample size as well as of trace element composition similarity) make it impossible to distinguish between groups at this level and at succeeding levels 6, 7, and 8. W h e n the same group was separated into nine clusters, however, homogeneous groups began to emerge. Table VIII indicates the number of samples from each locality within each of the clusters and demonstrates the benefit derived from increasing the number of clusters. E v e n with nine clusters, however, the program was unable to distinguish between Wisconsin and lower Michigan float coppers. T h e trace element compositions of samples within those groups appear to be very similar. Curiously enough, the Illinois float copper specimens, although geographically close and geomorphically related to the Lower Michigan and Wisconsin specimens, were, for the most part, easily separated from them. This grouping is apparent even in the five-cluster analysis and is indicative of the unique composition of float coppers from Illinois. After grouping specimens from five localities into nine clusters, it was necessary to recombine the clusters to evaluate the success of the


284


program in distinguishing among localities. A l l Illinois samples except two were found in homogeneous clusters 1 and 8. As previously mentioned, Wisconsin and L o w e r Michigan were l u m p e d together and comprised the bulk of cluster 7. Isle Royale samples clearly dominated cluster 6, entirely made up cluster 5, and shared cluster 4 with samples from Snake River. Snake River specimens exclusively made up clusters 2, 3, and 9, i n addition to making a strong showing in cluster 4. T h e complexity of these relationships is shown on the planar projection of the clusters (Figure 2), where Illinois clusters 1 and 8 are shown widely separated from the rest. W i t h i n the non-Illinois clusters, the division into localities


is discernible yet difficult to illustrate on the planar projection diagram. As a tool to trace artifact copper material to its most likely geographic and(or) geologic source, cluster analysis is useful, although the inability of this technique to distinguish easily among more than two or three localities at any one time makes it less valuable than the discriminant analysis. In an attempt to use cluster analysis in this manner, three artifact samples from the Lower Peninsula of Michigan were added individually to a cluster analysis of Illinois and Lower Michigan float copper specimens. A l l three samples clustered with the Lower Michigan float coppers. A further demonstration of K-means clustering uses samples from two copper mines in the U p p e r Peninsula of Michigan. T h e C h a m p i o n

20.0 Illinois A

16.0 12.0 8.0

S n a k e River

4.0 0

Isle R o y a l -4.0 L -5.4 Figure 2.

-3.6

-1.8

0

1.8

3.6

Projection into plane through the centers of clusters 7, 6, and 1.


14.

RAPP ET AL.

285

Native Copper Source Discrimination Table IX. Cluster Composition Comparisons Champion

Centennial

Two Clusters Cluster 1 Cluster 2

10 1

9 0


Six Clusters Cluster Cluster Cluster Cluster Cluster Cluster

1 2 3 4 5 6

5 1 0 2 3 0

3 4 1 0 0 1

NOTE: Modal values are in boldface type.

and Centennial mines were chosen for comparison because of their close proximity and geologic similarity. O f six clusters, five could be assigned to one mine or the other on the basis of cluster homogeneity and dominance of one mine; the sixth cluster (cluster 1) was mixed (Table IX). T h e relationship is evident, although not entirely explicit, in Figure 3. Two clusters, overlapping in only one area, emerged from the analysis, although the initial separation into two clusters was not definitive.

5.0

-1-0k , -2.0 -1.0 Figure 3.

.

,

.

.

.

0

1.0

2.0

3.0

4.0

I

5.0

Projection into plane through the centers of clusters 1, 2, and 5.


286


Summary and Conclusions W e have presented trace element data and statistical analyses using these data. T h e data base needs to be expanded with analyses of samples from the many additional known localities in North America. Other statistical techniques, such as the one used by Sigleo (2) for turquoise, will also be examined. It appears that the discriminant function d * is an important tool for archaeologists i n provenance studies and that K-means cluster analysis can be very helpful in studying the singularity of trace element


patterns for given localities.

Acknowledgments T h e neutron activation analyses were done as part of the Reactor Sharing Program under U . S . Department of E n e r g y Contract E - ( l l - l ) - 2 1 4 4 to the University ofWisconsin Reactor Facility (Richard Cashwell, director). Robert Woodhams assisted with the securing and sampling of some of the native copper samples. G r e g g Deutsch assisted with many of the computer calculations. S . E . Aschenbrenner provided valuable assistance in the development and maintenance of the data base. This project has been funded in part by Carleton College with a grant from the Bush Foundation and in part from private contributions to the University of Minnesota from the William A . Kings, the George Gibsons, and the Charles McCrossans.


14.

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Appendix Concentration Intervals and Distribution of Abundances Throughout These Intervals for 27 Elements and 10 Source Localities Range"

King? Cham Cent Isle Alas Αήζ Snak Wise Illn

Lomi


Silver (Ag) < 1.00000 100.00000 200.00000 300.00000 999999.00000

2 8 53 61 35

0 0 0 6 3

0 2 7 2 1

13 6 1 0 0

0 6 11 2 12

2 22 1 0 4

0 4 6 4 5

17 0 0 0 1

2 0 6 5 0

13 6 1 0 0

Totals

159

9

12

20

31

29

19

18

13

20

Arsenic (As)

Trace Element Discrimination of Discrete Sources of Native Copper

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