The mineralogy, geochemistry and microbiology of Cobalt-bearing mine tailings from the Cobalt Mining Camp in Northeastern Ontario, Canada
Bibliographic record
Abstract
Advancements in the field of biotechnology have proven bioleaching processes as an economic \nand environmentally safe form of mining. Most bioleaching studies to date however have been \nfocused on Fe, Cu, and Au-sulfidic mine tailings, rather than metalloid-rich, neutral-pH tailings. \nThis thesis will apply a multidisciplinary and multi-variate statistical approach to explore \nwhether the neutral, Co and As-rich tailings material within the Cobalt mining camp can be \nefficiently bioleached. Tailings material within 30-cm depth profiles from three tailings sites \n(sites A, B and C) were characterized for their mineralogical, chemical and microbial community \ncompositions, followed by the execution of bench-scale oxidative and reductive bioleaching \nexperiments. Tailings material from sites A, B and C are composed primarily of quartz, albite, \nclinochlore, calcite and dolomite with minor safflorite, arsenopyrite, erythrite and annabergite. \nThe material at site A contains on average more (sulf-)arsenides and higher concentrations of Fe \nthan site B. Site C however is altogether geochemically and mineralogically dissimilar, with the \npresence of distinct reduced and oxidized zones. Variations in the Co+As+Sb+Zn (Co#), Fe \n(Fe#), and total S (S#) have been identified as geochemical markers for the presence of Fe, Coarsenides versus secondary Co, Ni, Zn-arsenates, e.g. tailings material with a high Co# and low \nFe# tend to have a higher proportion of secondary arsenate minerals. In the tailings material of \nsites B and C, a lower average As valence coincides with a higher S#. Three distinct site-specific \ngroupings are observed for 1) the Co vs Fe and S#’s and 2) the microbial communities. The \nCobalt tailings are primarily composed of Actinobacteria and Proteobacteria and N, S, Fe, \nmethane, and (possible) As-cycling bacteria. The tailings from sites B and C have a larger \nabundance of Fe- and S-cycling bacteria (e.g. Sulfurifustis and Thiobacillus), of which are more \nabundant at greater depths, whereas the tailings of site A have a higher proportion of potential As-cycling and -resistant genera (e.g. Methylocystis and Sphingomonas). The microbial \ncommunities appear to be highly correlated to depth, S#, Fe#, pH, and the average valence of As. \nThe variation in the average valence of As correlates well with the abundances of N, S, Fe, and \nmethane-cycling bacteria (e.g. Nitrospira sp., the order Thermodesulfovibrionia, and \nMethylocystis sp.). Aerobic and anaerobic bioleaching experiments were conducted on three \nsamples (in duplicate) from each site, with six samples characterized by high and/or low Co, Fe \nand S#’s and three bulk, site-specific samples. The experiments used the tailings native consortia \nand had three methods/treatments. The first two were for chemolithotrophic and heterotrophic \nbacteria enrichment, with the third serving as a control or baseline experiment. The experiments \nran for 18-weeks, with (1) biweekly measurements of pH, Eh, and total and dissolved metals, \nand (2) analysis of the microbial community composition through 16S rRNA DNA extraction at \nexperiment completion. The highest abundance of As-reducing and -oxidizing genera (i.e. \nDelftia and Dechloromonas) were observed in the heterotroph-enrichments, which was \nadditionally characterized as having the highest Co and As recovery (0.94 and 29.8 %, \nrespectively). Samples that were composed of a higher proportion of Co, Ni, Zn-arsenates and \nFe, Co-arsenides were observed to be enriched in Fe-reducing bacteria or As-reducing and - \noxidizing genera, respectively. The enrichments were able to successfully shift the microbial \ncommunities to those involved in As-cycling. However, further work is required to determine \nwhat hindered the metal recovery process, i.e. future micro- and/or nano-scale studies may \nindicate that Co and As precipitated as Co-phosphates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".