Physical, chemical and ecological performance of Syncrude Canada Ltd’s Base Mine Lake
Bibliographic record
Abstract
Base Mine Lake (BML), the first commercial demonstration of water-capped tailings technology (WCTT) in the mineable oil sands in Alberta, Canada, was commissioned in December 2012, following seventeen years of transfer of fine tailings (FT) into West-in-pit. BML’s design basis is that over time, the FT will be physically isolated due to self-weight consolidation, the lake’s water quality will improve, and the lake will ultimately achieve targeted closure outcomes. Since commissioning, the lake has been intensively monitored to track the physical, chemical, and ecological parameters of the FT and the water cap in order to demonstrate the viability of WCTT in reclaiming FT. The FT is consolidating as predicted by finite-strain consolidation theory, reflected by increasingly distinct mudline and profile density increases with time. The water chemistry of BML is improving with decreasing concentrations of many key constituents below both acute and chronic protection of aquatic life guidelines. The lake’s ecosystem is developing, with the establishment of an aquatic invertebrate community. An adaptive management approach helps steward BML towards desired short and long-term objectives. Adaptive management actions have included alum treatment to address water turbidity, and removal of bitumen mats from the FT surface. Improvements observed in the trajectory of the physical, chemical, and ecological parameters for BML are consistent with its short-term objectives. Ongoing monitoring and adaptive management of BML will continue to ensure its long-term closure objectives are met.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".