Quantitative Mineral Resource Assessment of Copper, Molybdenum, Gold, and Silver in Undiscovered Porphyry Copper Deposits in the Andes Mountains of South America.: C.G. CUNNINGHAM, E.O. ZAPPETTINI, WALDO VIVALLO S., C.M. CELADA, J. QUISPE, D.A. SINGER, J.A. BRISKEY, D.M. SUTPHIN, M. GAJARDO M., A. DIAZ, C. PORTIGLIATI, V.I. BERGER, R. CARRASCO, and K.J. SCHULZ. U.S. Geological Survey Open-File Report 2008-1253. Pp. 282. 2008. Available on CD-ROM and online at .

2009 ◽  
Vol 104 (6) ◽  
pp. 887-887
Author(s):  
D. Cooke
2017 ◽  
Vol 85 ◽  
pp. 181-203 ◽  
Author(s):  
Jane M. Hammarstrom ◽  
Mark J. Mihalasky ◽  
Steve Ludington ◽  
Jeffrey D. Phillips ◽  
Byron R. Berger ◽  
...  

2012 ◽  
Vol 8 (5) ◽  
pp. 1599-1620 ◽  
Author(s):  
S. Wagner ◽  
I. Fast ◽  
F. Kaspar

Abstract. In this study, we assess how the anthropogenically induced increase in greenhouse gas concentrations affects the climate of central and southern South America. We utilise two regional climate simulations for present day (PD) and pre-industrial (PI) times. These simulations are compared to historical reconstructions in order to investigate the driving processes responsible for climatic changes between the different periods. The regional climate model is validated against observations for both re-analysis data and GCM-driven regional simulations for the second half of the 20th century. Model biases are also taken into account for the interpretation of the model results. The added value of the regional simulation over global-scale modelling relates to a better representation of hydrological processes that are particularly evident in the proximity of the Andes Mountains. Climatic differences between the simulated PD minus PI period agree qualitatively well with proxy-based temperature reconstructions, albeit the regional model overestimates the amplitude of the temperature increase. For precipitation the most important changes between the PD and PI simulation relate to a dipole pattern along the Andes Mountains with increased precipitation over the southern parts and reduced precipitation over the central parts. Here only a few regions show robust similarity with studies based on empirical evidence. However, from a dynamical point-of-view, atmospheric circulation changes related to an increase in high-latitude zonal wind speed simulated by the regional climate model are consistent with numerical modelling studies addressing changes in greenhouse gas concentrations. Our results indicate that besides the direct effect of greenhouse gas changes, large-scale changes in atmospheric circulation and sea surface temperatures also exert an influence on temperature and precipitation changes in southern South America. These combined changes in turn affect the relationship between climate and atmospheric circulation between PD and PI times and should be considered for the statistical reconstruction of climate indices calibrated within present-day climate data.


The Auk ◽  
1983 ◽  
Vol 100 (2) ◽  
pp. 390-403 ◽  
Author(s):  
Kenneth E. Campbell ◽  
Eduardo P. Tonni

Abstract The extinct family Teratornithidae contains the world's largest known flying birds. A new method of determining body weights of extinct birds, based on the size of their tibiotarsi, facilitates the estimation of the wing dimensions of these giant birds. An analysis of the bones of the teratorn wing shows that they closely resemble those of condors, suggesting that teratorns flew in a manner similar to these large New World vultures. The bones of the pelvic girdle and hindlimbs indicate that teratorns were probably agile on the ground, though better adapted for walking and stalking than running. We estimate that the largest teratorn, Argentavis magnificens, weighed 80 kg and had a wingspan of 6-8 m. It probably became airborne by spreading its huge wings into the strong, continuous, westerly winds that blew across southern South America before the elevation of the Andes Mountains and, once aloft, flew in the manner of condors.


2010 ◽  
Vol 38 (3) ◽  
pp. 270-287 ◽  
Author(s):  
K. Rasilainen ◽  
P. Eilu ◽  
T. Halkoaho ◽  
M. Iljina ◽  
T. Karinen

Author(s):  
Donald Singer ◽  
W. David Menzie

Now that all of the fundamental parts of a quantitative mineral resource assessment have been discussed, it is useful to reflect on why all of the work has been done. As mentioned in chapter 1, it is quite easy to generate an assessment of the “potential” for undiscovered mineral resources. Aside from the question of what, if anything, “potential” means, there is the more serious question of whether a decision-maker has any use for it. The three-part form of assessment is part of a system designed to respond to the needs of decision-makers. Although many challenging ideas are presented in this book, it has a different purpose than most academic reports. This book has the same goal as Allais (1957)—to provide information useful to decision makers. Unfortunately, handing a decision-maker a map with some tracts outlined and frequency distributions of some tonnages and grades along with estimates of the number of deposits that might exist along with their associated probabilities is not really being helpful—these need to be converted to a language understandable to others. This chapter summarizes how these various estimates can be combined and put in more useful forms. If assessments were conducted only to estimate amounts of undiscovered metals, we would need contained metal models and estimates of the number of undiscovered deposits. Grades are simply the ratio of contained metal to tons of ore (chapter 6), so contained metal estimates are available for each deposit. In the simplest of all cases, one could estimate the expected number of deposits with equation 8.1 (see chapter 8) and multiply it by the expected amount of metal per deposit, such as the 27,770 tons of copper in table 9.1, to make an estimate of the expected amount of undiscovered metal. As pointed out in chapter 1, expected amounts of resources or their values can be very misleading because they provide no information about how uncommon the expected value can be with skewed frequency distributions that are common in mineral resources; that is, uncertainty is ignored.


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