The Discovery History and Geology of Corani<subtitle>A Significant New Ag-Pb-Zn Epithermal Deposit, Puno Department, Peru</subtitle>
Bibliographic record
Abstract
There seems to be general consensus throughout much of the global mining industry that the supply of base and precious metals and some other commodities (e.g., ferrous metals, uranium) is reasonably well assured into the oreseeable future because increases in total resources continue to keep pace with or outstrip global consumption. The basic assumption is that market forces and technological advances will combine to promote and perpetuate this trend (e.g., Tilton, 2003; Crowson, 2008). Others disagree, however, andpredict that shortages are inevitable if metal consumption continues to escalate (Beaty, 2010). It is already becoming clear that many known resources seem unlikely to be mined, irrespective of commodity prices, because of their low grade and/or quality. Hence, many mineral resources that were uneconomic in the early 2000s are likely to remain so, both today and into the foreseeable future because of increases in both the direct (e.g., energy, labor) and indirect (e.g., environmental, social) production costs. This situation is being further exacerbated by the perceived decrease, over at least the past decade, in the discovery rate of base and precious metal resources measured in terms of both the number of major discoveries made and the exploration dollars spent per discovery (e.g., Dummett, 2000; Horn, 2002; Schodde, 2004). There is also a suggestion that the discoveries made are, on average, becoming both smaller and lower grade. Therefore, it seems reasonable to ask whether current exploration practices and success rates are going to be adequate to provide for the massive increases in metal consumption that world population growth, rising living standards, and rapid industrialization and urbanization in China, India, and other emerging markets appear to portend. For example, Rio Tinto's projections suggest that "by 2030 the additional supplyrequired will be equivalent to replicating the iron ore output of the Pilbara region of Australia every five years, adding another aluminium production complex the size of Canada's Saguenay every nine months, and developing another copper mine the size of Escondida in Chile each year. Future energrequirements are such that an entire Hunter Valley coal supply chain needs to be created each year plus a uranium mine the size of Ranger every four years" (Albanese, 2010, p. 7). Clearly, the exploration business has to become increasingly effective if it is to rise to the challenge of finding mineral resources of the right caliber to assure that this burgeoning demand can be adequately satisfied. Corani is a significant recently discovered large silver and base metal deposit situated within the Corani mining district in the central Andes of Peru. The deposit is located 200 km northwest of the city of Puno, between 4,675 and 5,260 m in elevation, and includes 12 mineral claims covering an area of 5.7 km2. The silver, lead, and zinc resources represent low- to intermediate-sulfidation–style epithermal mineralization hosted within 23 Ma rhyolitic crystal lithic tuffs. Potentially economic epithermal gold mineralization also occurs within the district and requires further exploration. Previous district production includes small-scale underground antimony mining in the 1940s and selective mining from high-grade silver veins during the 1960s. During the 1990s, limited drilling focused upon an epithermal gold zone at the southern limits of the current Corani deposit. Rio Tinto staked the district in 2003 as a porphyry copper system. The presence of such a system remains a possible deep source for the recognized epithermal mineralization. Bear Creek Mining Corporation acquired the district from Rio Tinto in 2005 and drilled the first discovery holes in 2006. To date, more than 93,640 m of diamond drilling has been completed in conjunction with extensive studies in order to understand the controls of the base and precious metal mineralization. Mineralization occurs as stockwork veins, fracture coatings, and breccias localized within a westerly-dipping listric fault complex resulting from regional extension. These ore-hosting structures cut the rhyolitic tuffs of the Quenamari Formation. Dominant mineral phases include quartz, barite, pyrite, sphalerite, galena, hematite, and freibergite. The total mineable reserve is 139.6 million tons (Mt) averaging 57.5 g/t Ag, 0.94 percent Pb, and 0.46 percent Zn, thus containing 8.03 t Ag (258 million ounces (Moz)), 1.31 t Pb (2.9 billion lbs.), and 0.65 t Zn (1.4 billion lbs.) recovered into concentrates. In addition, 145 Mt of lower grade material is maintained in resources. An understanding of the distribution of mineralization styles, defined by using metallurgical testing, mineragraphic analysis, and detailed core logging, is critical in unlocking the economic potential of the deposit and developing a three-dimensional model for mining. Of particular importance to future development is the overprinting by supergene mineral assemblages, including complex lead and/or barium phosphates and iron and/or manganese oxides.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".