Sulfate-based coagulants can suppress methanogenesis in treated oil sands fine tailings
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Bibliographic record
Abstract
Bitumen extraction from mined oil sands ore generates a large volume of fluid fines tailings (FFT) that must be incorporated into either aquatic or terrestrial reclamation landforms. Mine operators are developing various tailings technologies to accelerate FFT dewatering, including the addition of chemical coagulants and flocculants. However, the impacts of these coagulants and flocculants on biogeochemical processes in treated FFT are not fully understood. We conducted anaerobic batch experiments to examine the influence of different doses (i.e., 0, 500, 1000, and 1500 ppm) of sulfate-based coagulants, including aluminum sulfate (alum) [Al2(SO4)3∙nH2O], ferric sulfate (ferric) [Fe2(SO4)3∙nH2O], and calcium sulfate (gypsum) [CaSO4∙2H2O], on biogenic gas production and microbial communities in treated FFT. Our results show that sulfate addition stimulated microbial sulfate reduction, which inhibited methanogenesis in coagulated FFT relative to experimental controls. Sulfate depletion preceded increased methane production in the 500 ppm gypsum experiment, while larger ferric and alum doses produced higher sulfate concentrations and larger pH decreases. 16 S rRNA sequencing revealed that Comamonadaceae, Anaerolineaceae, and Desulfocapsaceae were the major bacterial families, while Methanoregulaceae and Methanosaetaceae dominated the archaeal families in all treatments. Precipitation of iron(II) sulfides limited dissolved hydrogen sulfide concentrations in experiments where Fe availability was not limited. Our results indicate that addition of sulfate-based coagulants can stimulate microbial sulfate reduction and suppress methanogenesis. However, resumption of methane production following sulfate depletion reveals complex interactions among biogeochemical reaction pathways. Overall, this study demonstrates that biogeochemical cycling of carbon, sulfur, and iron are important considerations for the development and implementation of tailings treatment technologies.
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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 | 0.001 |
| 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 it