GEOCHEMICAL IMPLICATIONS OF GYPSUM ADDITION TO OIL SANDS FLUID FINE TAILINGS: LABORATORY BATCH AND COLUMN EXPERIMENTS
Bibliographic record
Abstract
Large inventories of tailings in the Alberta Oil Sands have created the need for technologies that can accelerate dewatering of fluid tailings; however, knowledge of long-term implications from these technologies is limited. This research was split into two studies, examining: 1) temporal changes in porewater chemistry and gas production in gypsum-amended Fluid Fine Tailings (FFT), and 2) temporal and spatial changes in porewater chemistry in Centrifuged Fine Tailings (CFT) during successive freeze-thaw-evaporation cycles. An anoxic laboratory batch experiment was conducted, where differing gypsum amendments were added to FFT, and destructively sampled over 64 weeks. Methane measured in the headspace showed inconclusive results for the effect of gypsum on methanogenesis. Gypsum-amended FFT showed an increase in dissolved salts, with Na increasing up to 1.3 times (820–1,100 mg L−1) and Mg increasing up to 4.2 times (9.55–39.9 mg L−1) compared to the control. In the second experiment, six columns filled with CFT were subjected to three consecutive freeze thaw evaporation cycles, and sacrificially sampled before each thaw and evaporative period. Column mass decreased an average total of 28.5 kg with 72% of this attributed to runoff following the first thaw period. After this time, dissolved salts began accumulating in near the CFT surface, with Cl increasing up to 5.8 times (379–2,200 mg L−1), Na increasing up to 6.9 times (772–5,353 mg L−1), K increasing up to 15.6 times (16.1–251 mg L−1), and Mg increasing up to 94 times (22.0–2,069 mg L−1) compared to the initial CFT. Both studies revealed elevated porewater salt concentrations in gypsum-amended tailings, which could pose challenges for long-term reclamation of the oil sands tailings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".