Chemical mass transport between fluid fine tailings and the overlying water cover of an oil sands end pit lake
Bibliographic record
Abstract
Abstract Fluid fine tailings (FFT) are a principal by‐product of the bitumen extraction process at oil sands mines. Base Mine Lake (BML)—the first full‐scale demonstration oil sands end pit lake (EPL)—contains approximately 1.9 × 108 m3 of FFT stored under a water cover within a decommissioned mine pit. Chemical mass transfer from the FFT to the water cover can occur via two key processes: (1) advection‐dispersion driven by tailings settlement; and (2) FFT disturbance due to fluid movement in the water cover. Dissolved chloride (Cl) was used to evaluate the water cover mass balance and to track mass transport within the underlying FFT based on field sampling and numerical modeling. Results indicated that FFT was the dominant Cl source to the water cover and that the FFT is exhibiting a transient advection‐dispersion mass transport regime with intermittent disturbance near the FFT‐water interface. The advective pore water flux was estimated by the mass balance to be 0.002 m3 m−2 d−1, which represents 0.73 m of FFT settlement per year. However, the FFT pore water Cl concentrations and corresponding mass transport simulations indicated that advection rates and disturbance depths vary between sample locations. The disturbance depth was estimated to vary with location between 0.75 and 0.95 m. This investigation provides valuable insight for assessing the geochemical evolution of the water cover and performance of EPLs as an oil sands reclamation strategy.
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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 | 0.000 | 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".