Eagle’s Nest<subtitle>A Magmatic Ni-Sulfide Deposit in the James Bay Lowlands, Ontario, Canada</subtitle>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 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 it