Microbiology of a multi-layer biosolid/desulfurized tailings cover on a mill tailings impoundment
Bibliographic record
Abstract
The Strathcona Waste Water Treatment System (SWWTS; Sudbury, ON, Canada) has received mill tailings from Ni/Cu ore processing from 1970 to present. Demonstration-scale, multi-layer cover systems were installed on selected tailings deposition cells at the SWWTS. The cover systems are comprised of an upper layer of organic carbon-rich material, composed of a layer biosolids fertilizer along with composted municipal food and yard waste, then a layer of desulfurized, fine-grained tailings. Organic carbon components used in these covers promote microbial communities that consume O2, thus decreasing sulfide oxidation rates in the underlying tailings. The aim of this study was to investigate the microbiology of the cover systems and the underlying tailings, using a combination of culture-dependent (most probable number) and culture-independent (16S rRNA gene amplicon sequencing) techniques, and assess the impact of the organic component of the cover system four to six years after implementation. Most tailings samples were characterized by circumneutral bulk pH and low concentrations of dissolved metals. The presence of the organic cover resulted in elevated counts of sulfate-reducers (by two orders of magnitude, compared to control samples) immediately below the organic cover, as well as an increased abundance of heterotrophic species (∼108 cells g−1) at greater depth (∼4 m) in the tailings profile. Mineral-oxidizing microorganisms were also present in the tailings, with neutrophilic sulfur-oxidizers dominating the samples (mean ∼106 cells g−1). Relative abundances of sulfur- and/or iron-oxidizers determined by sequencing ranged from 0.5 to 18.3% of total reads (mean ∼5.6% in amended tailings) and indicated the presence of local microenvironments with ongoing sulfide oxidation. This work provides a detailed characterization of the microbiology of a tailings impoundment with an organic cover, highlighting the opportunities associated with monitoring microbial processes in such remediation systems.
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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".