Distribution of clay minerals in the process streams produced by the extraction of bitumen from Athabasca oil sands
Bibliographic record
Abstract
Abstract This study investigates the affinity of clay minerals in oil sands for the water‐continuous tailings and hydrocarbon‐continuous froth streams produced from the extraction of bitumen from oil sands. Clay minerals in oil sands processing impact bitumen flotation in separation vessels, emulsion formation during froth treatment, and fine tailings behaviour. X‐ray diffraction of oriented clay slides and random powder samples were used to quantify the clay minerals in the oil sands ore and process streams. Particle size distribution and clay activity balances were also conducted around the extraction process. The degree of partitioning during the conditioning and flotation stages in a batch extractor was determined by the surface properties of the clay minerals present. The water‐continuous tailings stream was further separated into fine and coarse tailings fractions through sedimentation. The bulk of the clay minerals reported to the fine tailings stream. Illite and mixed layered illite‐smectite partitioned less to the hydrocarbon‐continuous froth than kaolinite. Also, the illite‐smectite in the froth stream appeared to be different from the illite‐smectite in the water continuous streams.
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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".