Study of the Settling Characteristics of Tailings Using Nuclear Magnetic Resonance (NMR) Technique
Bibliographic record
Abstract
The oil sands mining and extraction processes in Canada produce large volumes of tailings that are a mixture of mainly water, clay, sand, chemicals and bitumen. This residue is deposited into tailings ponds where sand settles faster than fine clays which require many years to fully consolidate. Therefore, land reclamation and water recirculation become significant environmental impacts. The tailings settling rate depends on particle size, density and surface properties which can be modified by variations in the pH, salinity, and addition of flocculants and / or coagulants. Although plant scale developments have been made to improve tailings settling rates, there is a need for an on-site fast measurement of tailings settling characteristics to determine process modifications. This study uses the Nuclear Magnetic Resonance (NMR) technique to analyze variations in tailings settling properties. The results show the NMR technique as a potential online application to estimate the lifetime of a pond and to monitor oil sands processing.
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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) | 0.001 | 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".