Mineralogical Study of a Biologically-Based Treatment System That Removes Arsenic, Zinc and Copper from Landfill Leachate
Bibliographic record
Abstract
Mineralogical characterization by X-ray diffraction (XRD) and a high throughput automated quantitative evaluation of minerals by scanning electron microscopy (QEMSCAN) was conducted on samples from a sulphate-reducing biochemical reactor (BCR) treating high concentrations of metals (As, Zn, Cu) in smelter waste landfill seepage. The samples were also subjected to energy dispersive X-ray (EDX) analysis of specific particles. The bulk analysis results revealed that the samples consisted mainly of silicate and carbonate minerals. More detailed phase analysis indicated four different classes: zinc-arsenic sulphosalts/sulphates, zinc-arsenic oxides, zinc phosphates and zinc-lead sulphosalts/sulphates. This suggests that sulphates and sulphides are the predominant types of Zn and As minerals formed in the BCR. Sphalerite (ZnS) was a common mineral observed in many of the samples. In addition, X-ray point analysis showed evidence of As and Zn coating around feldspar and amphibole particles. The presence of arsenic-zinc-iron, with or without cadmium particles, indicated arsenopyrite minerals. Copper-iron-sulphide particles suggested chalcopyrite (CuFeS2) and tennantite (Cu,Fe)12As4S13. Microbial communities found in each sample were correlated with metal content to describe taxonomic groups associated with high-metal samples. The research results highlight mineral grains that were present or formed at the site that might be the predominant forms of immobilized arsenic, zinc and copper.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".