Biosulfides Precipitation in Weathered Tailings Amended with Food Waste-based Compost and Zeolite
Bibliographic record
Abstract
Tailings are mine wastes in the form of slurries stacked in mine sites abandoned after the exhaustion of ores. There are approximately 5000 abandoned mine sites in Korea, and tailings have become a serious environmental problem. Long-term environmental exposure of tailings can cause release of acidic and high concentrations of sulfate- and metal-contaminated water (acid mine drainage, AMD). Organic and/or inorganic amendments have been studied for AMD prevention and passive in situ treatment of pore water. This study tests locally available food waste-based compost as a viable amendment, in addition to the need for sustainable ways to dispose of compost, in response to a new environmental law. To examine the feasibility, three bioreactors were constructed, filled with mixtures of tailings, food waste-based compost, and zeolite. During the 4-wk experimental period, feeding water ormedium were poured in one reactor. The leachates were investigated in terms of chemistry and microbiology. Compared with the unamended reactor, the leachate from two mixture-filled reactors showed increased pH, formation of sulfate reduction conditions, and highly efficient metal removal. Black-colored precipitates observed at the end of the experiment suggested the formation of metal biosulfides, following the activity of sulfate reduction mediated by sulfate-reducing bacteria (SRB). Mineralogical analysis of these precipitates confirmed the presence of biosulfides, mainly of Fe and Pb. Moreover, microbial and molecular biological analyses revealed that several species of heterotrophic bacteria (SRB and iron-reducing bacteria) were present in the solids recovered from the bioreactors. Microbial consortium, such as SRB species (), and cellulosic-degrader ( sp.) were identified. This study provides promising results on the application potential of food waste-based compost for prevention of AMD generation and passive in situ treatment of pore water in weathered tailings in Korea and elsewhere.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".