Mitigation of Methane Emissions from Oil Sands Tailings by Redox Amendment: Mathematical Modeling of Empirical Observations
Bibliographic record
Abstract
Anaerobic biodegradation of fugitive diluent hydrocarbons in oil sands fine tailings (FT) sustains CH 4 emissions from tailings facilities and potentially from pit lakes, which impact the climate and effective tailings reclamation. We investigated the effectiveness of sulfate as a redox amendment to mitigate CH 4 production from FT containing ∼0.2% naphtha. FT were collected from four different locations (two methanogenically more active and two less active) in a tailings-containing pit lake. Microcosms incubated for ∼800 d suggested that labile hydrocarbons (∼35–38% of naphtha, supporting methanogenesis), including monoaromatics, n -alkanes, and iso -alkanes, were biodegraded under sulfate-reducing conditions in all FT with no significant CH 4 production. Although the extent of hydrocarbon biodegradation was similar, iso -alkanes were biodegraded faster in FT from sampling locations that were methanogenically less active in situ. A phenomenological model developed using zero-order kinetics predicted well naphtha biodegradation and sulfate reduction in microcosms. Using reported unrecovered naphtha input to an active tailings facility (Mildred Lake Settling Basin), the model suggested that sulfate amendment could reduce predicted CH 4 production from the labile naphtha fraction by ∼51–85%, potentially reaching 95–100% if sulfate reduction supported by other endogenous substrates was also considered. These findings can inform potential methane mitigation solutions for diluent (naphtha) affected 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".