Microbially-accelerated consolidation of oil sands tailings. Pathway I: changes in porewater chemistry
Bibliographic record
Abstract
Dispersed clay particles in mine tailings and soft sediments remain suspended for decades, hindering consolidation and challenging effective management of these aqueous slurries. Current geotechnical engineering models of self-weight consolidation of tailings do not consider microbial contribution to sediment behavior, however, here we show that microorganisms indigenous to oil sands tailings change the porewater chemistry and accelerate consolidation of oil sands tailings. A companion paper describes the role of microbes in alteration of clay chemistry in tailings. Microbial metabolism in mature fine tailings (MFT) amended with an organic substrate (hydrolyzed canola meal) produced methane (CH4) and carbon dioxide (CO2). Dissolution of biogenic CO2 lowered the pH of amended MFT to pH 6.4 vs. unamended MFT (pH 7.7). About 12% more porewater was recovered from amended than unamended MFT during 2 months of active microbial metabolism, concomitant with consolidation of tailings. The lower pH in amended MFT dissolved carbonate minerals, thereby releasing divalent cations including calcium (Ca(2+)) and magnesium (Mg(2+)) and increasing bicarbonate (HCO(-) 3) in porewater. The higher concentrations increased the ionic strength of the porewater, in turn reducing the thickness of the diffuse double layer (DDL) of clay particles by reducing the surface charge potential (repulsive forces) of the clay particles. The combination of these processes accelerated consolidation of oil sands tailings. In addition, ebullition of biogenic gases created transient physical channels for release of porewater. In contrast, saturating the MFT with non-biogenic CO2 had little effect on consolidation. These results have significant implications for management and reclamation of oil sands tailings ponds and broad importance in anaerobic environments such as contaminated harbors and estuaries containing soft sediments rich in clays and organics.
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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) | 0.000 | 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".