Using Specified Risk Materials-Based Peptides for Oil Sands Fluid Fine Tailings Management
Bibliographic record
Abstract
Fluid fine tailings are produced in huge quantities by Canada's mined oil sands industry. Due to the high colloidal stability of the contained fine solids, settling of fluid fine tailings can take hundreds of years, making the entrapped water unavailable and posing challenges to public health and the environment. This study focuses on developing value-added aggregation agents from specified risk materials (SRM), a waste protein stream from slaughterhouse industries, to achieve an improved separation of fluid fine tailings into free water and solids. Settling results using synthetic kaolinite slurries demonstrated that, though not as effective as hydrolyzed polyacrylamide, a commercial flocculant, the use of SRM-derived peptides enabled a 2-3-fold faster initial settling rate than the blank control. The pH of synthetic kaolinite tailings was observed to be slightly reduced with increasing peptides dosage in the test range (10-50 kg/ton). The experiments on diluted fluid fine tailings (as a representation of real oil sands tailings) demonstrated an optimum peptides dosage of 14 kg/ton, which resulted in a 4-fold faster initial settling rate compared to the untreated tailings. Overall, this study demonstrates the novelty and feasibility of using SRM-peptides to address intractable oil sands fluid 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".