Geochemical Stability of Oil Sands Tailings in Mine Closure Landforms
Bibliographic record
Abstract
Oil sands surface mining in Alberta has generated over a billion cubic metres of waste, known as tailings, consisting of sands, silts, clays, and process-affected water that contains toxic organic compounds and chemical constituents. All of these tailings will eventually be reclaimed and integrated into one of two types of mine closure landforms: end pit lakes (EPLs) or terrestrial landforms with a wetland feature. In EPLs, tailings deposits are capped with several metres of water while in terrestrial landforms, tailings are capped with solid materials, such as sand or overburden. Because tailings landforms are relatively new, past research has heavily focused on the geotechnical and biogeochemical characteristics of tailings in temporary storage ponds, referred to as tailings ponds. As such, the geochemical stability of tailings landforms remains largely unknown. This review discusses five mechanisms of geochemical change expected in tailings landforms: consolidation, chemical mass loading via pore water fluxes, biogeochemical cycling, polymer degradation, and surface water and groundwater interactions. Key considerations and knowledge gaps with regard to the long-term geochemical stability of tailings landforms are identified, including salt fluxes and subsequent water quality, bioremediation and biogenic greenhouse gas emissions, and the biogeochemical implications of various tailings treatment methods meant to improve geotechnical properties of tailings, such as flocculant (polyacrylamide) and coagulant (gypsum) addition.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".