Modeling the Effects of Seasonal Weathering on Centrifuged Oil Sands Tailings
Bibliographic record
Abstract
The oil sands industry employs different technologies at pilot and commercial demonstration scales in order to improve the dewatering rate of fluid fine tailings. Of these technologies, centrifugation has advanced to the commercial scale and is playing a major role in the fluid fine tailings management strategy. However, centrifuge technology on its own may not develop the required strength to ensure fine tailings can be incorporated into dry landform reclamation, which requires water contents close to their plastic limit. Hence, it is paramount to combine more than one technology to maximize post-depositional dewatering. Management of the tailings deposit to promote seasonal weathering (freeze–thaw, evaporation and self-weight consolidation) can promote further dewatering. Properly assessing the contributions of the seasonal weathering components is vital to optimizing this strategy. Using the geotechnical properties of centrifuged tailings, the effects of seasonal weathering on tailings were modeled under two different freezing temperature gradients. A coupled analysis combining FSConsol and Unsatcon was used to simulate the deposition scenario similar to the laboratory. The modeling results were found to match the laboratory response reasonably well, indicating the coupled approach proposed in this manuscript is valid and helps to predict the seasonal weathering effects on dewatering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".