J. Great Lakes Res. 30 (Supplement 1):162–184 Internat. Assoc. Great Lakes Res., 2004 Local, Regional, and Global Implications of Elemental Mercury in Metal (Copper, Silver, Gold, and Zinc) Ores: Insights from Lake Superior Sediments
Bibliographic record
Abstract
ABSTRACT. Anthropogenic inventories for copper (229 ± 89 ug/cm 2, N = 30), and mercury (470 ± 307 ng/cm 2, N = 25) in Lake Superior sediments are much greater than inventories in remote lakes (Cu 50 ± 31 ug/cm 2, Hg 64 ± 34 ng/cm 2, N = 16) that receive inputs largely from long-distance atmospheric sources. Whereas the absolute concentration of mercury in Lake Superior sediments is not high (80–110 ng/g), enrichment ratios along coastal margins indicate industrial sources. An example of previously unreported mining-related inputs comes from native copper mining on the Keweenaw Peninsula. Around the peninsula, sediment inventories for mercury, silver, and copper are highly correlated and can be traced back to shoreline tailing piles, smelters, and parent ores. Elemental mercury occurs as a natural amalgam or solid solution substitution in native metal (copper, silver, gold) deposits and associated gangue minerals (e.g., sphalerite, ZnS) at µg/g or higher concentrations. Native copper stamp mills discharged more than 364 million metric tons of “stamp sand ” tailings, whereas copper smelters refined five million metric tons of copper, liberating together at least 42 metric tons of mercury. Release of trace mercury from Lake Superior mining deserves regional attention as preliminary estimates resemble EPA Region #9 patterns and could help explain the 4–7 fold sediment inventory discrepancies. We show that the Keweenaw situation is not unique geographically, as mineral-bound trace mercury is commonplace in U.S. and Canadian Greenstone Belts and of worldwide occurrence in precious (gold, silver) and massive base metal (copper, zinc) ore deposits.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.174 | 0.096 |
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".