Bibliographic record
Abstract
The transportation of oil sand via slurry pipeline reduces downstream processing costs because some separation of bitumen from the sand/clay matrix occurs during transit (conditioning). However, there is currently no real-time method for assessing the extent of conditioning inside a pipeline. We investigated bitumen droplet size analysis as a technique for determining the extent of conditioning in a slurry line by conducting field tests at Syncrude Canada Ltd.'s oil sand operation in Fort McMurray, Alberta. Slurry was withdrawn from two different pipelines at five specially designed sampling stations and the liberated bitumen droplets were allowed to float through a water-filled viewing chamber. The droplets were videotaped and analyzed using particle sizing software to determine the average droplet size and shape. This data was correlated to feed grade, slurry temperature and transport distance to determine if a relationship existed between the physical slurry properties and the droplet data. Results suggest that droplet size analysis can be used to assess the extent of conditioning inside an oil sand slurry pipeline in real time. This technology could be incorporated into the control scheme of an oil sand processing circuit to improve separation efficiency and reduce costs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".