Ebullition enhances chemical mass transport across the tailings-water interface of oil sands pit lakes
Bibliographic record
Abstract
Base Mine Lake (BML) was the first commercial-scale demonstration oil sands pit lake established in northern Alberta, Canada. Recent studies indicate that ebullition enhances internal mass loading of dissolved constituents during settlement and dewatering of methanogenic fluid fine tailings (FFT) below the overlying water cap. Here, we describe results of integrated field measurements and numerical modelling to (i) determine potential for ebullition and enhanced mixing within BML, and (ii) assess impacts on chemical mass transport across the tailings-water interface. We observed sharp increases in [CH4(aq)] with depth from <0.1 mg L−1 immediately above the interface to >60 mg L−1 over the upper 1.5 to 3.0 m of FTT. Thermodynamic modelling revealed that maximum [CH4(aq)] values represent 60 to 80% of theoretical saturation, and corresponding total dissolved gas pressures approach or exceed fluid pressures. These findings supported integration of enhanced mixing into one-dimensional (1-D) advective-dispersive transport models, which substantially improved upon previous simulations of conservative tracer (i.e., Cl−) profiles and chemical mass fluxes. The models revealed a positive relationship between CH4(aq) saturation and enhanced mixing, showing that ebullition enhances internal mass loading. This information has potential to inform ongoing assessments of pit lake performance and support improved closure and reclamation planning at oil sands mines.
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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 | 0.001 |
| 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".