Legacy copper/nickel mine tailings potentially harbor novel iron/sulfur cycling microorganisms within highly variable communities
Bibliographic record
Abstract
The oxidation of sulfide-bearing mine tailings catalyzed by acidophilic iron and sulfur-oxidizing bacteria releases toxic metals and other contaminants into soil and groundwater as acid mine drainage. Understanding the environmental variables that control the community structure and metabolic activity of microbes indigenous to tailings (especially the abiotic stressors of low pH and high dissolved metal content) is crucial to developing sustainable bioremediation strategies. We determined the microbial community composition along two continuous vertical gradients of Cu/Ni mine tailings at each of two tailings impoundments near Sudbury, Ontario. 16S rRNA amplicon data showed high variability in community diversity and composition between locations, as well as at different depths within each location. A temporal comparison for one tailings location showed low fluctuation in microbial communities across 2 years. Differences in community composition correlated most strongly with pore-water pH, Eh, alkalinity, salinity, and the concentration of several dissolved metals (including iron, but not copper or nickel). The relative abundances of individual genera differed in their degrees of correlation with geochemical factors. Several abundant lineages present at these locations have not previously been associated with mine tailings environments, including novel species predicted to be involved in iron and sulfur cycling.IMPORTANCEMine tailings represent a significant threat to North American freshwater, with legacy tailings areas generating acid mine drainage (AMD) that contaminates rivers, lakes, and aquifers. Microbial activity accelerates AMD formation through oxidative metabolic processes but may also ameliorate acidic tailings by promoting secondary mineral precipitation and immobilizing dissolved metals. Tailings exhibit high geochemical variation within and between mine sites and may harbor many novel extremophiles adapted to high concentrations of toxic metals. Characterizing the unique microbiomes associated with tailing environments is key to identifying consortia that may be used as the foundation for innovative mine-waste bioremediation strategies. We provide an in-depth analysis of microbial diversity at four copper/nickel mine tailings impoundments, describe how communities (and individual lineages) differ based on geochemical gradients, predict organisms involved in AMD transformations, and identify taxonomically novel groups present that have not previously been observed in mine tailings.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".