Bibliographic record
Abstract
The global mining industry has invested a large amount of capital in mineral exploration and development over the past 15 years in an effort to ensure that sufficient resources are available to meet future increases in demand for minerals. Exploration data have been used to identify specific sites where this investment has led to a significant contribution in global mineral supply of cobalt or where a significant increase in cobalt production capacity is anticipated in the next 5 years. This report provides an overview of the cobalt industry, factors affecting mineral supply, and circumstances surrounding the development, or lack thereof, of key mineral properties with the potential to affect mineral supply. Of the 48 sites with an effective production capacity of at least 1,000 metric tons per year of cobalt considered for this study, 3 producing sites underwent significant expansion during the study period, 10 exploration sites commenced production from 1995 through 2008, and 16 sites were expected to begin production by 2013 if planned development schedules are met. Cobalt supply is influenced by economic, environmental, political, and technological factors affecting exploration for and production of copper, nickel, and other metals as well as factors affecting the cobalt industry. Cobalt-rich nickel laterite deposits were discovered and developed in Australia and the South Pacific and improvements in laterite processing technology took place during the 1990s and early in the first decade of the 21st century when mining of copper-cobalt deposits in Congo (Kinshasa) was restricted because of regional conflict and lack of investment in that country's mining sector. There was also increased exploration for and greater importance placed on cobalt as a byproduct of nickel mining in Australia and Canada. The emergence of China as a major refined cobalt producer and consumer since 2007 has changed the pattern of demand for cobalt, particularly from Africa and Australasia. Chinese companies are increasingly becoming involved in copper and cobalt exploration and mining in Congo (Kinshasa) and Zambia as well as nickel, copper, and other mining in Australia and the South Pacific. Between 2009 and 2013, mines with a cumulative capacity of more than 100,000 metric tons per year of cobalt were proposed to come into production if all sites came into production as scheduled. This additional capacity corresponds to 175 percent of the 2008 global refinery production level. About 45 percent of this cobalt would be from primary nickel deposits, about 32 percent from primary copper deposits, and about 21 percent from primary cobalt deposits. By 2013, about 40 percent of new capacity was expected to come from the African Copperbelt; 38 percent, from Australia and the South Pacific countries of Philippines, Indonesia, New Caledonia, and Papua New Guinea; 11 percent, from other African countries; 5 percent, from North America; and 6 percent, from other areas.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".