Environmental impact of mineralised black shales
Why this work is in the frame
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Bibliographic record
Abstract
Black shales are sedimentary rocks containing >0.5% of organic carbon. They host polymetallic deposits which have been mined for Cu, Ni, Zn, Mn, P, Mo, V, U, Au and PGE (platinum group elements). Even sub-economic occurrences provide potential risk of acid rock drainage when exposed to oxic surface environment. The natural acid neutralisation potential varies depending on the adjacent rock units, especially on the presence of calcareous rocks. The chemical and mineralogical composition of black shale is reflected in the quality of the surface waters and groundwater. Cu, Ni, Pb, U and Zn are recognised as major pollutants though the environmental impact is characteristically polymetallic just like the black shale occurrences. Hence, the environmental impacts have to be evaluated in each occurrence. The Proterozoic Ni–Zn–Cu–Co deposit at Talvivaara, Finland, is reviewed in more detail as an example of a large, low-grade deposit that is currently exploited with open pit mining and a bioleaching process, together with the Proterozoic Central African Copperbelt, the Cambrian U-Mo deposits in Sweden, the Cambrian Ni–Mo–PGE deposits in China and the Cambrian-Ordovician U deposits in South-Korea, the Devonian Ni–Zn–PGE occurrences in Yukon, Canada, and Kentucky, USA, and the Permian Cu-Ag deposits in Poland and Germany. The mineralised horizons may be merely few centimetres thick like in Yukon or hundreds of metres thick like at Talvivaara. Both natural and anthropogenic environmental impacts of black shales are reviewed world-wide, and based on the overview of the state-of-the-art an integrated research approach is suggested for the comprehensive assessment of the risk. Black shales are natural sources of soil and water contamination. At Talvivaara, the geochemical background includes higher than average concentrations of Ni, Cu, Zn and Mn in glacial till, peat, surface waters and groundwater as well as in stream and lake sediments. Bioaccumulation by plants has been reported in China and Korea. Even endemic diseases have been proposed to be linked with the contamination derived from the weathering and leaching of harmful elements from black shale. Anthropogenic actions exposing the black shale bedrock and associated soils to oxic conditions further intensifies acid rock drainage. Regional or nation-wide mapping of the black shales is recommended to detect potential risk areas. Finland has recently completed the country-wide mapping program of black shales with airborne geophysics integrated with geological, petrophysical and geochemical studies. The black shale database is actively used in regional planning and by environmental authorities, research institutes and consulting companies. In the case of the historical black shale mining areas like in the Kupferschiefer in Germany, restoration measures have been applied to prevent further acid mine drainage. In active and future mining projects, a comprehensive environmental impact assessment with effective monitoring programmes and closure plans play a crucial role in the prevention of acid mine drainage from the black shale -associated deposits.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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