The Influence of Tailings Composition on Flocculation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is hard to treat fine tailings resulting from oil sands extraction processes. No current fine tailings treatment technology can completely eliminate tailings ponds, despite considerable efforts to address the slow settling of fines and to facilitate the consolidation of sediments. Some treatments use coagulants and coarse solids to form composite or consolidated tailings; others use polymer flocculants. The performance of polymer flocculants is evaluated empirically, with fine tailings often being considered as a black box. Some fundamental studies use single clay systems, such as kaolinite suspended in water, as models to evaluate the performance of polymer flocculants. While it is easier to relate polymer performance to specific conditions in these simpler systems, it is difficult to translate these results to the treatment of the much more complex tailings environment. For the rational design of polymer flocculants, one must understand the interactions between polymers and the several components in tailings. With this information in hand, one can optimize the molecular structure of polymer flocculants to treat oil sands tailings efficiently. In this review, we summarize the published research on flocculation performance within the context of complex fine tailings systems. Furthermore, we describe the compositional complexity of mature fine tailings to help the design of more representative model tailings systems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it