Controls on Mass and Thermal Loading to an Oil Sands End Pit Lake from Underlying Fluid Fine Tailings
Bibliographic record
Abstract
End pit lakes (EPLs) are a relatively new strategy proposed for reclaiming oil sands surface mines.An EPL is formed within a depleted mine pit, with fluid fine tailings (FFT) stored below a water cover.Fluid fine tailings are a by-product of the oil sands bitumen extraction process with high water contents, low bearing capacities, and elevated concentrations of various constituents.This thesis considers mass and heat transfer between the FFT and overlying water cover at the first EPL, Base Mine Lake (BML).The study objectives were: (1) characterize the FFT thermal properties and the thermal regime in BML;(2) assess FFT settlement rates and characteristics; and (3) evaluate a range of potential mechanisms for mass and heat movement, including diffusion or conduction, and mixing of the FFT due to unstable density profiles or fluid movement within the water cover.These objectives were achieved through a combination of field investigations, laboratory testing, and numerical modelling, and the results were published in three manuscripts comprising the main body of the thesis.Overall, FFT is the largest (Cl) mass source to the BML water cover.The dominant transport mechanism was advective mass transport or convective heat transport due to tailings settlement; however, tailings disturbance near the FFT-water interface may also contribute to mass release.The predicted pore water fluxes based on the advective or convective regimes were similar to previously estimated FFT settlement rates and decreased throughout the studied period from approximately 1.46 m/a in 2013 to 2014, to 0.73 m/a in 2014 to 2015.Declining advection rates indicate that diffusive mass transport and conductive heat transfer will likely become more significant in the future.The results also confirmed that EPL design should consider the size of the water cover, volume and characteristics of the FFT, and operational controls, as these factors will likely influence EPL success as a sustainable reclamation landscape.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 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 teacher head, 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".