Relationships Between the Microbial Composition and the Geochemistry and Mineralogy of the Cobalt-Bearing Legacy Mine Tailings in Northeastern Ontario
Bibliographic record
Abstract
Mine tailings host dynamic biogeochemical processes that can mobilize a range of elements from the host material and release them into the environment through acidic, neutral, or alkaline mine drainage. Here we use a combination of mineralogical, geochemical, and microbiological techniques that provide a better understanding of biogeochemical processes within the surficial layers of neutral cobalt and arsenic-rich tailings material at Cobalt, ON, Canada. Tailings material within 30-cm depth profiles from three tailings sites (sites A, B, and C) were characterized for their mineralogical, chemical and microbial community compositions. The tailings material at all sites contains (sulf)arsenides (safflorite, arsenopyrite), and arsenates (erythrite and annabergite). Site A contained a higher and lower amount of (sulf)arsenides and arsenates than site B, respectively. Contrary to site A and B, site C depicted a distinct zoning with (sulf)arsenides found in the deeper reduced zone, and arsenates occurring in the shallow oxidized zone. Variations in the abundance of Co+As+Sb+Zn (Co#), Fe (Fe#), total S (S#), and average valence of As indicated differences in the mineralogical composition of the tailings material. For example, material with a high Co#, lo Fe# and high average valence of As commonly have a higher proportion of secondary arsenate to primary (sulf)arsenide minerals. Microbial community profiling indicated that the Cobalt tailings are primarily composed of Actinobacteria and Proteobacteria, and known N, S, Fe, methane, and possible As-cycling bacteria. The tailings from sites B and C had a larger abundance of Fe and S-cycling bacteria (e.g., Sulfurifustis and Thiobacillus ), which are more abundant at greater depths, whereas the tailings of site A had a higher proportion of potential As-cycling and -resistant genera (e.g., Methylocystis and Sphingomonas ). A multi-variate statistical analysis showed that (1) distinct site-specific groupings occur for the Co # vs. Fe #, Co# vs. S#’s and for the microbial community structure and (2) microbial communities are statistically highly correlated to depth, S#, Fe#, pH and the average valence of As. The variation in As valence correlated well with the abundance of N, S, Fe, and methane-cycling bacteria. The results of this study provide insights into the complex interplay between minerals containing the critical element cobalt, arsenic, and microbial community structure in the Cobalt Mining Camp tailings.
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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 | 0.001 |
| 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 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".