Overcoming disciplinary solitude: The archaeology and geology of native copper in Eastern North America

Although native copper has attracted the scholarly attention of both geologists and archaeologists since the middle of the 19th century, it is only recently that native copper studies have benefited from interdisciplinary research. This history of disciplinary solitude can be traced to the professionalization of the two fields in the early 20th century, an era in which crossdisciplinary communication began to wane. The effects of this phenomena resulted in the development of a model of aboriginal native copper procurement by archaeologists that did not take into account the geological literature, which had long identified numerous—rather than a single—source of native copper in North America. In this article, the author discusses how this disciplinary solitude developed and how it resulted in the creation of a dominant model for native copper procurement that constrained our understanding of aboriginal lifeways for generations. The author also considers how increasing collaboration between geologists and archaeologists since the 1970s has led to the reevaluation of an old model of native copper procurement that has been uncritically accepted by most archaeologists for over a century and a half. © 2007 Wiley Periodicals, Inc.     

Trace‐element analysis of native copper: The view from Northern Minnesota,USA

Native copper, both finished artifacts and worked pieces, are relatively common in pre‐Contact sites of northeastern Minnesota. Fifteen artifact samples and seven sources of native copper were submitted for trace‐element analysis as a test of the procedures developed by G. Rapp, Jr. and colleagues (2000). Although the specific sources chosen had high typicality probabilities, very low posterior probabilities preclude confidence that the artifacts were accurately sourced. Potential problems include incomplete characterization of several sources, as well as the limited number of sources in the database. Of more interest than identification of specific sources is the selection of apparent source pairs. Michipicoten (eastern Lake Superior) and Isle Royale (western Lake Superior) are often chosen together; Weyerhauser Mine (Wisconsin) and the Champion Mine (Keewenaw Peninsula) may form another pair. Future research should concentrate on identifying patterns of sources in the region rather than focusing on specific mines. © 2007 Wiley Periodicals, Inc.     

A digital procedure for ground water recharge and discharge pattern recognition and rate estimation

A digital procedure to estimate recharge/discharge rates that requires relatively short preparation time and uses readily available data was applied to a setting in central Wisconsin. The method requires only measurements of the water table, fluxes such as stream baseflows, bottom of the system, and hydraulic conductivity to delineate approximate recharge/discharge zones and to estimate rates. The method uses interpolation of the water table surface, recharge/discharge mapping, pattern recognition, and a parameter estimation model. The surface interpolator used is based on the theory of radial basis functions with thin-plate splines. The recharge/discharge mapping is based on a mass-balance calculation performed using MODFLOW. The results of the recharge/discharge mapping are critically dependent on the accuracy of the water table interpolation and the accuracy and number of water table measurements. The recharge pattern recognition is performed with the help of a graphical user interface (GUI) program based on several algorithms used in image processing. Pattern recognition is needed to identify the recharge/discharge zonations and zone the results of the mapping method. The parameter estimation program UCODE calculates the parameter values that provide a best fit between simulated heads and flows and calibration head-and-flow targets. A model of the Buena Vista Ground Water Basin in the Central Sand Plains of Wisconsin is used to demonstrate the procedure.     

A method of computing the gravitational field of an axially symmetric flat galaxy

A method has been developed for computing the gravitational force field of an axially symmetric flat galaxy from its surface mass density. The method is simple to program, fast, and accurate. An inversion formula is derived that allows computation of surface density from rotation curves by use of any method that converts density to force. The method is compared with a method of Clutton-Brock that utilizes Hankel transforms of Laguerre functions.     

Stratigraphy and thermoluminescence dating of Late Glacial cover sand in Denmark

In western Jutland deposits with a texture and structure similar to Dutch cover sands have been found. Investigations have been carried out on the lithology and the lithostratigraphic sequences. Pollen analysis is used for biostratigraphic correlation, and ages have been obtained by the radiocarbon method as well as by thermoluminescence techniques using both quartz and eldspar. The five sites are presented separately and the data are integrated into a general stratigraphic outline of the Late Glacial cover-sand sequence in Jutland. During the latest part of the Upper Pleniglacial and the earliest Late Glacial, older cover-sand type was deposited. Locally this sediment was also deposited during the later parts of the Late Glacial. Younger cover-sand type was deposited primarily during the Early Dryas and Late Dryas, but some sand of this type was also laid down during the Allerød and the earliest Holocene. The palaeobotanical records are discontinuous, but plant growth was probably present throughout the Late Glacial; only during the Altered did the deposition of sand decrease long enough to allow a relatively stable vegetational development. The investigation shows that the sequence of events in Jutland is similar to the Dutch record.     

Tetrahedral compression in (Mg,Fe)SiO3 orthopyroxenes

Single-crystal structure determinations at pressure have shown that the structural response of synthetic (Mg_0.6Fe_0.4)SiO₃ orthopyroxene to compression is the same as that previously observed in MgSiO₃ orthoenstatite. At pressure below ～4?GPa there is no significant compression of the SiO₄ tetrahedra, while above ～4?GPa the tetrahedra decrease in volume as a result of Si?O bond shortening. A study of the compressional behaviour of synthetic FeSiO₃ orthoferrosilite also shows the same behaviour below 4?GPa, but studies at higher pressures are precluded due to the transformation of the sample to the higher density C2/c high-clinoferrosilite polymorph. A further single-crystal study to 6?GPa of a Ca²⁺-containing natural orthopyroxene shows that chemical substitution of, primarily, Al³⁺ and Ca²⁺ into the structure of orthopyroxene inhibits the initial rapid compression of the M2?O3 bonds observed in the synthetic samples, and no significant tetrahedral compression is observed in this sample. Raman data collected from synthetic MgSiO₃ orthoenstatite show that there is a change in the rate of change of frequency with pressure, δν/δP, between 3.5 and 6.0?GPa, but no changes in the number of observed bands. These observations indicate a non-symmetry-breaking change in the properties of the orthoenstatite, which is associated with the change in compression mechanism observed using X-ray diffraction techniques at this pressure.     

Predicting conductance due to upconing using neural networks

Artificial neural networks (ANNs) were developed to accurately predict highly time-variable specific conductance values in an unconfined coastal aquifer. Conductance values in the fresh water lens aquifer change in response to vertical displacements of the brackish zone and fresh water-salt water interface, which are caused by variable pumping and climate conditions. Unlike physical-based models, which require hydrologic parameter inputs, such as horizontal and vertical hydraulic conductivities, porosity, and fluid densities, ANNs can "learn" system behavior from easily measurable variables. In this study, the ANN input predictor variables were initial conductance, total precipitation, mean daily temperature, and total pumping extraction. The ANNs were used to predict salinity (specific conductance) at a single monitoring well located near a high-capacity municipal-supply well over time periods ranging from 30 d to several years. Model accuracy was compared against both measured/interpolated values and predictions were made with linear regression, and in general, excellent prediction accuracy was achieved. For example, although the average percent change of conductance over 90-d periods was 39%, the absolute mean prediction error achieved with the ANN was only 1.1%. The ANNs were also used to conduct a sensitivity analysis that quantified the importance of each of the four predictor variables on final conductance values, providing valuable insights into the dynamics of the system. The results demonstrate that the ANN technology can serve as a powerful and accurate prediction and management tool, minimizing degradation of ground water quality to the extent possible by identifying appropriate pumping policies under variable and/or changing climate conditions.     

A neural network model for predicting aquifer water level elevations

Artificial neural networks (ANNs) were developed for accurately predicting potentiometric surface elevations (monitoring well water level elevations) in a semiconfined glacial sand and gravel aquifer under variable state, pumping extraction, and climate conditions. ANNs "learn" the system behavior of interest by processing representative data patterns through a mathematical structure analogous to the human brain. In this study, the ANNs used the initial water level measurements, production well extractions, and climate conditions to predict the final water level elevations 30 d into the future at two monitoring wells. A sensitivity analysis was conducted with the ANNs that quantified the importance of the various input predictor variables on final water level elevations. Unlike traditional physical-based models, ANNs do not require explicit characterization of the physical system and related physical data. Accordingly, ANN predictions were made on the basis of more easily quantifiable, measured variables, rather than physical model input parameters and conditions. This study demonstrates that ANNs can provide both excellent prediction capability and valuable sensitivity analyses, which can result in more appropriate ground water management strategies.