Gold Mining and Submarine Tailings Disposal: Review and Case Study
Why this work is in the frame
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Bibliographic record
Abstract
Environmental impacts associated with submarine tailings disposal (STD) of gold mine wastes vary widely among the relatively few cases studied. The principal contaminants of concern surrounding most gold mines are arsenic, mercury, and cyanide, although antimony, thallium, lead, zinc, and copper may also be important in particular mines. The mineralogy and ore processing techniques associated with different kinds of gold deposits may strongly influence the outcome of STD. Native gold and its associated minerals are generally less toxic than sulfide-mineral gold, in which the gold is incorporated into sulfide minerals in conjunction with other trace elements. Sulfide gold tailings placed in seawater may be particularly dangerous where ore processing includes oxidation by roasting or aggressive chemical leaching, which transforms the sulfide minerals into relatively unstable oxides and oxy-hydroxides.The case study of the Newmont Minahasa Raya gold mine in Indonesia highlights some of the dangers of gold mine STD. Local villagers observed fish kills shortly after the beginning of STD operations, and they also noted fine red sediment resembling the tailings smothering corals on reefs adjacent to the tailings disposal site. Tailings from this mine dispersed from the intended STD depth of 82 m up to nearby coral reefs, and dispersal extended up to 3.5 km from the end of pipe. Unstable arsenic phases in the tailings accounted for at least 32% of total arsenic in the mine tailings, and less than 10% of total arsenic in fluvially derived marine sediments. Mercury in the submarine tailings was methylated in approximately the same proportions as mercury from artisanal gold mines using mercury amalgamation and in uncontaminated nearshore marine sediments near a watershed with similar bedrock geology. Methyl mercury derived from tailings was incorporated into the local food chain, probably via benthic invertebrates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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