Geological Evolution and Exploration Geochemistry of the Boomerang Lake Unconformity-type Uranium Prospect, Northwest Territories, Canada
Bibliographic record
Abstract
VOLCANIC-ASSOCIATED and sedimentary-exhalative massive sulfide deposits on land account for more than one-half of the world's total past production and current reserves of zinc and lead, 7 percent of the copper, 18 percent of the silver, and a significant amount of gold and other by-product metals (Singer, 1995). A new source of these metals is now being considered for exploitation from deep-sea massive sulfide deposits. Because the oceans cover more than 70 percent of the Earth's surface, many expect the ocean floor to host a proportionately large number of these deposits. However, there have been few attempts to estimate the global mineral potential. Significant accumulations of metals from hydrothermal vents have been documented at some locations (e.g., 91.7 Mt of 2.06% Zn, 0.46% Cu, 58.5 g/t Co, 40.95 g/t Ag, and 0.51 g/t Au in the Atlantis II Deep of the Red Sea: Mustafa et al., 1984; Nawab, 1984; Guney et al., 1988). Even more metal is contained in deep-sea manganese nodules. Current estimates in the U.S. Geological Survey (USGS) mineral commodities summaries indicate a global resource of copper in deep-sea nodules of about 700 Mt. In the Pacific "high-grade" area, an estimated 34,000 Mt of nodules contain 7,500 Mt of Mn, 340 Mt of Ni, 265 Mt of Cu, and 78 Mt of Co (Morgan, 2000; Rona, 2003). A number of countries, including China, Japan, Korea, Russia, France, and Germany, are actively exploring this area.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".