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Judith Goldman
Poetics Program
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Alibaba.com  Global trade starts here…™

About 181 results: Other Ore (48)
Tantalum Ore Coltan for sale

FOB Price:
US $30,000 - 35,000 / Metric Ton Get Latest Price
Min.Order Quantity:
20 Metric Ton/Metric Tons
Supply Ability:
50 Metric Ton/Metric Tons per Month
Port:
Dar Es Salaam
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T/T
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Place of Origin:                     Congo, The Democratic Republic Of The               Model Number:             Ta205                Shape:             Lump
Concentrate Or Not:            Non-concentrate

Specifications

1. GENERAL DESCRIPTION 

COMMODITY: COLTAN/TANTALITE

PURITY 35% OF COLTAN/TANTALITE.

CONTRACT TBA

PACKING DETAILS: Export Package (METAL DRUMS) (OCC) 

ORIGIN: Africa. 

PRICE $50 USD. Per kilogram 

CURRENCY OF PAYMENT: USD (United States Dollars)

QUANTITY: 50 MTS [metric tonnes] (50,000kgs) Subject to our final confirmation.

HISTORY: Clean, Clear, no liens and non Criminal Origin

Minerals Education Coalition online store.  12” Gold/Gem Pan.  Gold-Bearing Sand Refill. “If it can’t be grown, it must be mined.” 

Perhaps this is a false dichotomy, at least when it comes to “goldfarming.”  “Goldfarming”: posterchild for digital capitalism, working the seam of a not-so-strange twist: whereby virtual currency, such as gold coins, that circulates in massively multiplayer online role-playing games taking place in a virtual persistent worlds, are exchanged in the “real” economy.  Companies that goldfarm employ workers to play World of Warcraft and other online games for 10-12hr shifts at extremely low pay, to do the stultifying, repetitive labor of amassing the gold needed to buy in-game items such as weapons.  The virtual goldpieces are sold for “real” money on third-party websites, mimicking, unsurprisingly, the asymmetrical transactional dynamics of most goods we purchase: almost all of the gold farmed is bought by Americans and Western Europeans, who avoid expending their time advancing through lower-skill levels by purchasing readymade tokens or other virtual resources allowing them to level-up their avatars.  (One company, Game Dollar LLC, trades in the gameworld as “peons4hire.”) 

Although Ghirda works in Romania, the computers and the internet connection he uses are paid for by a company in northern California.  Gamersloot.net is one of a growing number of firms taking advantage of a boom in online computer games by opening “virtual sweatshops.”   

Autonomist theorists of labor Michael Hardt and Antonio Negri characterize mining, the “extraction of raw materials,” along with agriculture, as primary production; it is both subordinate to and transformed by the immaterial labor of providing services and manipulating information, or tertiary production, which has assumed a dominant position in contemporary capitalism. 

Hardt and Negri’s thought on how the postindustrial communications economy reorganizes all forms of production globally offers insight into the political economic postmodernization of resource extraction.  Yet it nonetheless makes mining seem anachronistic, the opposite of the labor of the cognitariat. 

“‘Depending on how you define “sweatshop,”’” says Patrick Bernard, head of Gamersloot.net.

“Capitalist development is always at the same time a process of underdevelopment,” as feminist theorist of reproductive labor Silvia Federici points out.  Which is to say immaterial labor dominates the global economy not just by transforming the industrial organization of mining.  Because the means of production for this privileged form of work – the material digital interface – depend absolutely on the spoils of mining, it brings “a tremendous increase of exploitation” into being.  Federici writes, “There is a continuum between the computer worker and the worker in the Congo who digs coltan with his hands.”  Far from anachronism, with the rise of digital culture, mining has become more crucial, more prevalent, more intensive now than at any time in history.  Accelerated, like everything else.  More so. 

How should we look at goldfarming?  How does virtual, as opposed to material, mining link to immaterial labor’s dominance?  As behind-the-scenes labor servicing internet gamers, goldfarming relates to Antonio Negri’s important notion of the real subsumption of society in contemporary capitalism: all social relations, as he argues, have been incorporated into capitalist circuits; social life itself produces surplus value for capital.  There is no time of “life” distinct from time of “work.” 

A 24-year-old UK gamer: “‘You could spend time farming gold, say, 20 real-life hours. Or you could go to work for two hours and earn the money to buy the gold.  If I'm playing I want to play, not do boring tasks.’” Leisure, like wage labor, measured in time.  And play is too much work. 

No doubt, with ubiquitous computing, in digitally networked societies, the division between work and leisure has grown fuzzy.  But goldfarming occupies a shadowy place of exploitation as an adjunct to privileged social subjects’ pleasure, even if that pleasure might itself also be exploited.  Perhaps goldfarming offers leverage for preserving while rethinking distinctions between consumption and production even as both are forms of labor.

Ironies abound, as they say, in the hall of mirrors between virtual and real economies, fictive and actual currencies: thus a self-employed Australian gold farmer with a gold fetish invested the fortune she made selling virtual gold in real gold, which she insured and kept in a safe.  Such ironic substitutions pale in comparison to ever more ironic metonymies: how prisoners working open pit coalmines in northeast China by day were then forced by prison guards to farm gold in online games all night.  

“‘One of the things that gets me up in the morning is knowing that the people in Romania are making a decent living out of what I'm doing, and that without it some of them might have turned bad.’” 

Task primitives.  See all your data from any source in one platform.  Automatically updated, real-time metrics.  Data management.  Metadata.  Data handlers.  Data brokers.  Data warehouse.  Database.  Data dredging.  Noisy data.  Data scrubbing.  Data gaps.  Data rot. 

Almost 52 million computers, 36 million monitors, and 152 million mobile devices were disposed of in the US in 2010. The US generated over 3.4 million tons of e-waste in 2012.  E-waste: hazardous waste.   

Data shadow. 

A 2009 Frontline story focuses initially on e-waste exported from the US and Europe to Ghana – it is burned by boys in Agbogbloshie, a slum referred to by locals as “Sodom and Gemorrah” – then abruptly shifts to reporters’ transactions surrounding salvageable hard-drives sold secondhand at open air markets: student investigators buy a lot of drives; it turns out they contain easily harvestable private financial data.  One drive has been recovered from the major US military contractor Northrup Gruman: it contains details about sensitive, multi-million dollar government contracts from the Defense Intelligence Agency, NASA, and Homeland Security.  The boys set fire to old foam on top of computers to melt the plastic, gathering scraps of copper and iron to sell.  The tinier pieces are picked up with magnets.  Investigators also visit a site in Guiyu, China, where women cook circuit boards to salvage computer chips with trace amounts of gold.  Any number of US companies claims outright to recycle electronic waste safely and locally: in fact, the waste is shipped in containers to China and Hong Kong. 

From an article on the site Money Crashers – Your Guide to Financial Fitness: “Every time you replace one of your electronic devices, it’s your responsibility to be sure your old one gets recycled properly.”

Media theorist Jonathan Sterne frames the foreshortened computer lifecycle: through industry-planned obsolescence and coercive mandating of upgrades, as object of cultural fantasies of newness, progress, and perfected functionality, in incremental cultural shifts towards greater wastefulness, the computer became a disposable consumer item.  “Extended product responsibility” is a policy measure through which “companies are held responsible for the physical management of their products, the costs of the waste created by the products, and for informing consumers about the possible environmental effects of a product at different times in its lifecycle.”  Sterne is critical: “The state’s managerial interest in waste is directly political…even environmental regulations designed to restrict some of the damage…also help perpetuate the cycle of computer purchase, use, warehousing, and eventual disposal.”  The whole culture of incompatibility and obsolescence must go.

From Wired: “The easier it is to disassemble something, the more likely it is to be worth someone’s time to recycle it…Smaller, lighter products can be tricky to take apart, and yield a lower volume of raw materials…In the past, computers were designed to be relatively easy to disassemble…You could swap out dead parts and batteries, add more memory if it got sluggish, even replace a motherboard.  But in the mid-2000s, Apple introduced the ultra-thin, ultra-light MacBook Air and the industry enthusiastically followed with…devices that, while slim, were very difficult to repair due to the construction compromises required to achieve that svelte profile.”

Shipping containers will be set up at more than 40 collection points in Kenya, each functioning as an independent small business that purchases e-waste from newly trained individual collectors.  When a shipping container is full, business owners sell the contents to a central hub where the e-waste is processed into its components.  Then they are sold once again, this time back to the technology industry for reuse.  Each stage is designed to be profitable for participants, from individual collector to collection point to hub.

If, according to the World Bank, Africa is “poor” “because of bad government, corruption, and civil strife,” in Global Shadows: Africa in the Neoliberal World Order (2006), James Ferguson counters with the simple fact that the countries the World Bank and IMF see as the biggest failures, even those with active civil wars, were most successful at attracting foreign capital in the 90s through the turn of the century.  Nations where offshore oil is extracted, like Angola, provide a “clean” corporate set-up: the industry imports all its equipment and materials, employs very few local workers, and brings in skilled labor from elsewhere, sequestered in a gated compound.  Capital pours in, through a specific mode of doing business.

“New innovations in the organization of mining make some nonpetroleum forms of mineral extraction more oil-like in their social consequences,” Ferguson points out.  He speaks of the spatial enclaving of production – mineral-rich patches efficiently exploited by private firms, with security provided on an “as needed” basis by specialized corporations.  Direct control over a fairly limited piece of ground and secure access to an external market.  Thus, “unruliness” can facilitate the flexible, opportunistic forms of deregulated enterprise he terms “extractive neoliberalism.”

The largest country in sub-Saharan Africa, the Democratic Republic of Congo is estimated to hold $24 trillion of raw mineral deposits, including 70% of the world’s land supply of tantalum ore in the form of columbite-tantalite or “coltan.”  Tantalum is used for high performance capacitors in electronics and is an especially key component of compact devices such as laptops and cell phones.  In 2000, the price of tantalum spiked tenfold, rising from $30/lb to $300/lb.  The sale of coltan has been a driver of the Congolese Civil wars that, from 1997 on, have claimed 6 million people.  Mining in conflict areas involves manual labor, simple tools, and easily accessed surface deposits.  Armed groups forcing labor, including child labor and labor by pregnant and nursing women, are prevalent, as are prostitution, sexual slavery, and rape.  Militias sell the coltan generated by many artisanal miners on the spot market. 

The shift from farming to mining has produced severe food insecurity, despite the country’s rich soil and favorable climate.  Key supplies of coltan lie in national parks with UNESCO World Heritage Site status.  Thousands of families moved into the parks to mine, relying on bushmeat: thousands of elephants, antelopes, buffalos, and chimps.  In 3 years, 90% of the lowland gorillas were eaten.  All of the elephants were gone.  By 2001, hunting trips had begun to last a week and often nothing was caught.  Miners began to eat tortoises, birds, and small animals.

Sulfur, Hemmimorphite, Zincite, Smithsonite, Franklenite, Pyrargyrite, Cerargyrite, Halite, Bauxite, Chalcopyrite, Boronite, Enargite, Cuprite, Malachite, Azurite, Chrysocolla, Chalcocite, Gold, Yittrium Sulfate, Europium, Alunite, Orthoclase, Nephelite, Leucite, Apophullite, Fluorite, Cryolite, Vesuvianite, Lepidolite, Dolomite, Magnesite, Espomite, Spinel, Olivine, Pyrope, Biotite, Talc, Pyroxenes, Zinc Cadmium Sulfide, Zinc Silicate, Realgar, Orpiment, Niccolite, Cobalite, Arsenopyrite, Tetrahedrite, Gadolinium Sulfate, Tebrium, Yittrium Silicate, Monzanite, Orthite, Galena, Cerussite, Anglesite, Quartz, Calcite, Gypsum, Apatite, Aragonite, Rutile, Titanite, Pyromorphite, Wavellite, Cassiterite , Sphalerite, Magnetite,  Limonite, Pyrargyrite, Cerargyrite, Euxenite

Minerals in a computer.

Achille Mbembe writes of necropolitical sovereignty: “My concern is those figures of sovereignty whose central project is…the generalized instrumentalization of human existence and the material destruction of human bodies and populations.”  He hones in on Africa, where, he observes, “the political economy of statehood dramatically changed over the last quarter of the twentieth century.  Many African states can no longer claim a monopoly on violence…[or] on territorial boundaries…Urban militias, private armies, armies of regional lords, private security firms, and state armies all claim the right to exercise violence or to kill.”  He describes such a chaotically distributed economy of necropolitical sovereignty as the emergence of war machines: “War machines are made up of segments of armed men that split up or merge with one another depending on the tasks to be carried out and the circumstances…Their relation to space is mobile…War machines function by borrowing from regular armies while incorporating new elements well adapted to the principle of segmentation and deterritorialization.” 

Mining becomes the epicenter and engine of this violence: “New linkages have…emerged between war making, war machines, and resource extraction.”  “Zones in which specific resources are extracted” are “militia economies,” “privileged spaces of war and death.”  Thus, over against spatial mobility and deterritorialization, necropolitical paramilitary occupations are also hyper-territorialized: war machines, like Ferguson’s “flexible firms,” have a brutally literal, fixed relation to the piece of ground they seize.  Mbembe calls this “the new geography of resource extraction.”

From Robert Smithson, “A Sedimentation of the Mind: Earth Projects”: “The manifestations of technology are at times less ‘extensions’ of man, than they are aggregates of elements.  Even the most advanced tools and machines are made of the raw matter of the earth.  Today’s highly refined technological tools are not much different in this respect from those of cavemen.”

“A consumer sets her cell phone to vibrate, a function enabled by wolframite.”

From “A Sedimentation of the Mind: Earth Projects”: “The names of minerals and minerals themselves do not differ from each other, because at the bottom of both the material and the print is beginning of an abysmal number of fissures.  Words and rocks contain a language that follows a syntax of splits and ruptures.  Look at any word long enough and you will see it open up into a series of faults, into a terrain of particles each containing its own void.”

The half-life of thorium is about 14 billion years.  The half-life of uranium is up to 4.5 billion years.  Rare earths always occur alongside the radioactive elements thorium and uranium.

The partitioning is not performed by humans, but by the clustering algorithm.  Hence clustering is useful in that it can lead to the discovery of previously unknown groups within the data.

Mountain near Acme  Kayford Mountain  Cherry Pond Mountain  Mountain near Rawl  Pickering Knob 1  Kelly Mountain Mountain near Ransom  Mountain near Thacker  Mountain near Eckman  Mountain near Monson  Pine Ridge  Ford Mountain  Mountain near Hurley  Hunt Knob  Big Hill 

The commonplace book.  Logic of the literary extract: in Renaissance humanism, reading as mining.  Cultivated extraction parses a text by topic: memorable passages – new fanglings of adages, gems with facets added – dug out individually in the course of study and categorized by topic: compiled knowledges of love; enmity; religion.  In manuscript.  Indexed according to a personal controlled vocabulary, metadata.  Dream of a research project: comb through tailings left from stripmining: data that didn’t become information, failed data.  Not stripmining – urban mining, recycling: literary gold as renewable resource.  Mine a manufactured item for materials usable in one’s own writing.  Commonplacing: a way to manage and condense information.  To claim cultural literacy.  To become a sparkling conversationalist: find the topical gem. 

A hundred tonnes of platinum used each year in the United States for industrial and automotive catalysts requires that a hundred million tonnes of crude ore be dug up and processed in South Africa.

Databases may become “data tombs” – data archives that are seldom revisited.  Consequently, important decisions are often made based not on the information-rich data stored in databases but rather on a decision-maker’s intuition. Mining is a vivid term characterizing the process that finds a small set of precious nuggets from a great deal of raw material.  The widening gap between data and information calls for a systematic development of data mining tools that will turn data tombs into “golden nuggets” of knowledge.

“In mining, ‘gangue’ is the commercially worthless material that surrounds…a wanted mineral in an ore deposit…For any particular ore deposit…the concentration of the wanted mineral in the gangue material will determine whether it is commercially viable to mine that deposit…Minerals once thought of as gangue and dumped as tailings may later find a commercial use.  When this happens the old dumps are often reworked to extract the wanted mineral.”

Remaking cultures of extraction.  New forms of reading and knowledge-creation.  “What is the social life of algorithms?”  Users of information services will benefit from new machine learning technologies that mine new knowledge by integrating and analyzing very large amounts of widely distributed data to uncover and report upon subtle relationships and patterns of events that are not immediately discernible by direct human inspection.  “Data needs to be imagined as data.” 

“We are deluged by data.  Human attention has become the precious resource.  So, we must find ways to automatically analyze the data, to automatically classify it, to automatically summarize it, to automatically discover and characterize trends in it, and to automatically flag anomalies.” 

A search on “mining” in the Eighteenth Century Catalog Online database (Gale Cengage Learning: 33 million pages of 180,000 books).  Father Jaime Hernandez, 1755: A philosophical and practical essay on the gold and silver mines of Mexico and Peru; Containing the nature of the ore, and the manner of working the Mines; the Qualities and use of Quicksilver; the cleansing and refining these Metals: With many useful observations concerning the assay of metals, the manner of conveying them to Europe, and remarks on the gold mines in Hungary, and those of Asia and Africa.  Translated from a letter wrote in Spanish, by Father James Hernandez of the Society of Jesus; employed by his Catholic majesty to write the natural history of the West Indies.

Describing a mine in Alto Peru (before it was Bolivia), at Potosi: “They work in perpetual Darkness, never knowing whether it is Day or Night; these being Places to which the Sun never has any Access.  [The passages] are not only always dark, but also very cold, and infested with a thick disagreeable Air, which is apt to make such as go in giddy, and as it were Sea-sick; as it happened to myself, being sick at the Stomach, and troubled with Retchings to vomit.  The Men always work there by Candle-light, one Party by Day, and another relieving them at Night.  The Ore is generally hard, and they break it with Iron Crows…then they carry it up on Ladders…Ten Fathom high; and at the End of them is another of the same Height…there are many Ladders to ascend.  A Man carries up half an Hundred Weight in a Sort of Bag, fastened before him, and hanging on his Back, and thus they go up, Three and Three.  The foremost of them carries a lighted Candle, tied to his Thumb, that they may see, (for there is no Light of Heaven) and they holding fast with both Hands, ascend above One Hundred and Fifty Fathoms, which is very dreadful.” 

A data system has the potential to generate thousands or even millions of patterns, or rules.  Are all of these patterns interesting?  What makes a pattern interesting?  Can a data mining system generate all of the interesting patterns?  Can a data mining system generate only the interesting patterns?  An interesting pattern represents knowledge.  Several objective measures of pattern interestingness exist. 

Like a lot of the images that I've used, it's iconography that's just kind of exhausted and overmined. It's the visual equivalent of strip-mining, how subculture really traffics in overexhausted iconography, especially with aggression and morbidity. 

The kinds of knowledge mined.  The ability to mine knowledge at multiple granularities.  If a user feels that the generalization reaches too high a level, the threshold can be increased. This corresponds to drilling down along the attribute.  At times, a user may want to drill through the cube to the raw data for one or two particular dimensions.  How would you support this feature?

Mountaintop removal as data mining.  A new data mining inquiry language.  Some online analytical processing systems offer additional drilling operations.  Drill-across executes queries involving (i.e., across) more than one fact table.  The drill-through operation uses relational standardized query language facilities to drill through the bottom level of a data cube down to its back-end relational tables.  This is an outdated metaphor. 

Most subcultures keep notching up the irony levels. That's the paradox: that the chosen images are things that have been completely strip-mined and that all value has already been leached out.  And then you try to update them so people invest that much more belief into them. 



A new necropolitics: of mountains.  Mountaintop removal mining.  Or: bombing.  The fast-growing, tremendous amount of data has far exceeded our human ability for comprehension without powerful tools.   In 2005, more than 1.8 billion lb. of explosives were used in surface mining in West Virginia and Kentucky.  To get at the thin seams of coal.  Mountaintop removal as a form of interpretation.  As an interpretive frame.  The arrogance of big data.  Blow through the cloud to discover knowledge: concentrated energy.  Coal seams.  A new mountain face as interface. 

Quick Details
Place of Origin:            Congo, The Democratic Republic Of
Blackish and fine grayish in color:
Packaging & Delivery
Packaging Detail:        To be packed and sealed in drums of 20 to 25 kilos.
Delivery Detail:           30 - 60 days
Specifications
tantalite 32% max, and niobium 12% minimum
We are looking for interested buyers of our product coltan / Tantalite ore of DRC origin with minimum 25% - 32% tantalite.
Our terms and conditions are based on 100% Ex- Ware House payments at SDV Kigali ware house.
Client should be approved by SDV officials to be reliable before the supply.
Contact us for more details.
Director

A customer relationship manager at AllElectronics may want to compare two groups of customers—those who shop for computer products regularly and those who rarely shop for such products.  The resulting description provides a general comparative profile of these customers: 80% of the customers who frequently purchase computer products are 20-40 years old and have a university education, whereas 60% of the customers who infrequently buy such products are seniors or youths, and have no university degree. Drilling down on a dimension like occupation, or adding a new dimension like income level, may help to find even more discriminative features between the two classes. 

A frequently occurring subsequence, such as the pattern that customers, tend to purchase first a laptop, followed by a digital camera, and then a memory card, is a (frequent ) sequential pattern.

<ilovemountains.org> hosts The National Monument to Lost Mountains, an online memorial that uses Google Earth software to commemorate the 500 mountains fallen to mountaintop removal mining, including before-and-after photos and a high resolution tour of a removal site.  The site also contains an interactive map and an Excel database of destroyed mountains. 

Feeding back to create enhanced user experience, what makes big data “big data” is not just that it is large – petabytes large – but that one can search, aggregate, and cross-reference data sets in “real time.”  It is data produced by the tracking technologies of cybernetic capitalism, profiling users and collecting records of microgestures that have not been voluntarily surrendered.  “Flecks of identity.” 

Typically, a human analyst must sort through the deviations to ascertain which represent real intrusions.  Metarules may be based on the analyst’s experience, expectations, or intuition regarding the data.  Constraint-based mining strategies can be used to help direct the mining process towards patterns that match users’ intuition. 

“Objective measures of pattern interestingness” v. human “intuition.”  The machine proclivity for arbitrary correlation v. the human habitus- and craft-based “hunch.”  We use data mining to exit human biases but return to human divination: human to machine to human prospecting.  How does one find a proper data query language?  Is there a significant pattern?  Apophenia: might a pattern be discerned where there is none?

An extract, from Agricola, De re metallica (1556), Book II: The miner and a discourse on the finding of veins:  There are many great contentions between miners concerning the forked twig, for some say that it is of the greatest use in discovering veins, and others deny it.  Those who manipulate and use the twig grasp the fork with their hands, clenching their fists, it being necessary that the clenched fingers should be held toward the sky in order that the twig should be raised at that end where the two branches meet.  Then they wander hither and thither at random through mountainous regions. It is said that the moment they place their feet on a vein the twig immediately turns and twists, and so by its action discloses the vein.

Industrial mining creates replacement mountains: the standardized, perfect cones of slag heaps or spoil tips, prone to landslip.  From what kind of world is big data collected?  Once it is mined: what world does it shape?  Mountaintop removal creates “holler fills”: overburden is pushed into surrounding valleys.  Fill material.  Leveling. 

Here, ground truth is the ideal clustering that is often built using human experts. 

Pickering Knob 2  Glen Alum Mountain  Belcher Mountain  Mountain near Prestonburg  Ridge w of Elkins Branch  Mountain near Clintwood  Mountain near Prater  Sandy Ride near Lynn Spring Gap  Summit near Poplar Gap  Pike County Airport Ridge  Ridge E of Wanda  Compton Mtn  Elk Mountain  Burnt Knob  Mann Knob  Hoover Knob  Mountain near Kirk  Maxwell Gap  Summit Ridge E of Spruce Pine Branch  Mountain near Cowen  Mountain near Harriet  Mountain near Hernshaw  Workman Knob

The learning algorithm is active in that it can purposefully query a user (e.g., a human oracle) for labels.  Oracle (e.g., human annotator). 

Therefore a miner, since we think he ought to be a good and serious man, should not make use of an enchanted twig, because if he is prudent and skilled in the natural signs, he understands that a forked stick is of no use to him, for as I have said before, there are the natural indications of the veins which he can see for himself without the help of twigs.  So if Nature or chance should indicate a locality suitable for mining, the miner should dig his trenches there; if no vein appears he must dig numerous trenches until he discovers an outcrop of a vein.

Finding the “natural signs.”  Reading for a vein.  Or not.  Just cut through. 

If you are low on gold, I recommend you try this gold making guide
If you want to level mining with [Smelting] only
You should always smelt your ores to gain a few free skill-ups
Or title to surface where a tunnel is a run
You can level your character to Level 85 in 5 days with this Guide.
Download the following add-on: Gathermate2.
You can just buy any low level gloves from a vendor and enchant that one with this Enchant
When you mined all of it, just go outside of the zone where you can see the little yellowish arrows, mine a few Tin and head back to the red zone, because it’s most likely that the Tin Veins there are already respawned

Red’s done.

The blue is done.

Works Cited

Agricola, Georgius.  De re metallica.  (1556)  Herbert Clark Hoover and Lou Henry Hoover, trans.  New York: Dover, 1950. 

Alibaba.com.  www.alibaba.com.  Last accessed: 11 Aug 2015.

Ayres, Robert U., Ayres, Leslie W., and Warr, Benjamin.  “Is the U.S. Economy Dematerializing?  Main Indicators and Drivers.”  In Joroen C.J.M.                                     van den Bergh and Marco A. Janssen, eds.  Economics of Industrial Ecology: Materials, Structural Change, and Spatial Scales.  Cambridge: MIT Press, 2004. 57-93.

Bollen, Christopher.  “15 For 09: Banks Violette.”  Interview Magazine (Dec 2008).  https://www.google.com/search?client=safari&rls=en&q=www.interviewmagazine.com+banks+violette&ie=UTF-8&oe=UTF-8.  Last accessed: 11 Aug 2015.

Bonnington, Christina.  “Our E-Waste Problem Is Ridiculous, and Gadget Makers Aren’t Helping.”  Wired.  8 December 2014.  http://www.wired.com/2014/12/product-design-and-recycling/.  Last accessed: 11 Aug 2015.

boyd, danah and Crawford, Kate.  “Critical Questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon.”  Information, Communication & Society 15:5 (June 2012).   662-679.

Butler, Kiera.  “Your Smartphone’s Dirty, Radioactive Secret.”  Mother Jones (Nov/Dec 2012).  http://www.motherjones.com/environment/2012/11/rare-earth-elements-iphone-malaysia?page=2.  Last accessed: 11 Aug 2015.

Davis, Rowenna.  “Welcome to the New Goldmines.”  The Guardian.  4 Mar 2009.  http://www.theguardian.com/technology/2009/mar/05/virtual-world-china.  Last accessed: 11 Aug 2015.

Dias, Elizabeth.  “First Blood Diamonds, Now Blood Computers?”  Time.  24 July 2009 http://content.time.com/time/world/article/0,8599,1912594,00.html.  Last accessed: 9 Aug 2015.

Electronics TakeBack Coalition.  “Facts and Figures on E-Waste and Recycling.”  June 2014.  http://www.electronicstakeback.com/wp-content/uploads/Facts_and_Figures_on_EWaste_and_Recycling.pdf.  Last accessed: 11 Aug 2015. 

“E-Waste Championship Moves to Developing World.” SustainableBusiness.com.  7 Jan 2014.  http://www.sustainablebusiness.com/index.cfm/go/news.display/id/25422.  Last accessed: 11 Aug 2015.

Federici, Silvia.  “Precarious Labor: A Feminist Viewpoint.” (2008)  In the Middle of the Whirlwind.  https://inthemiddleofthewhirlwind.wordpress.com/precarious-labor-a-feminist-viewpoint/.  Last accessed: 11 Aug 2015. 

Ferguson, James.  Global Shadows: Africa in the Neoliberal World Order.  Durham: Duke University Press, 2006. 

Fleming, Ryan.  “After Converting $75K into Gold Bars, a Gold Farmer Loses Her House, and Is Sued.”  Digital Trends.  15 Aug 2013.  http://www.digitaltrends.com/gaming/after-a/. Last accessed: 11 Aug 2015.

“Ghana: Digital Dumping Ground.”  Frontline/World.  23 June 2009.   http://www.pbs.org/frontlineworld/stories/ghana804/video/video_index.html.  Last accessed: 9 Aug 2015. 

“Gangue.”  Wikipediahttps://en.wikipedia.org/wiki/Gangue.  Last accessed: 11 Aug 2015. 

Han, Jiawei, Kamber, Michelle, and Pei, Jian.  Data Mining: Concepts and Techniques.  Waltham: Morgan Kaufmann Publishers, 2012. 

Hardt, Michael and Negri, Antonio.  Commonwealth.  Cambridge: Harvard University Press, 2009. 

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