Microbially-accelerated consolidation of oil sands tailings. Pathway II: solid phase biogeochemistry
Bibliographic record
Abstract
Consolidation of clay particles in aqueous tailings suspensions is a major obstacle to effective management of oil sands tailings ponds in northern Alberta, Canada. We have observed that microorganisms indigenous to the tailings ponds accelerate consolidation of mature fine tailings (MFT) during active metabolism by using two biogeochemical pathways. In Pathway I, microbes alter porewater chemistry to indirectly increase consolidation of MFT. Here, we describe Pathway II comprising significant, direct and complementary biogeochemical reactions with MFT mineral surfaces. An anaerobic microbial community comprising Bacteria (predominantly Clostridiales, Synergistaceae, and Desulfobulbaceae) and Archaea (Methanolinea/Methanoregula and Methanosaeta) transformed Fe(III) minerals in MFT to amorphous Fe(II) minerals during methanogenic metabolism of an added organic substrate. Synchrotron analyses suggested that ferrihydrite (5Fe2O3. 9H2O) and goethite (α-FeOOH) were the dominant Fe(III) minerals in MFT. The formation of amorphous iron sulfide (FeS) and possibly green rust entrapped and masked electronegative clay surfaces in amended MFT. Both Pathways I and II reduced the surface charge potential (repulsive forces) of the clay particles in MFT, which aided aggregation of clays and formation of networks of pores, as visualized using cryo-scanning electron microscopy (SEM). These reactions facilitated the egress of porewater from MFT and increased consolidation of tailings solids. These results have large-scale implications for management and reclamation of oil sands tailings ponds, a burgeoning environmental issue for the public and government regulators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".