A Two Level Fractional Factorial Design to Test the Effect of Oil Sands Composition and Process Water Chemistry on Bitumen Recovery from Model Systems
Bibliographic record
Abstract
Abstract To overcome the compositional variability inherent to natural oil sands we use extraction tests with model oil sands (MOS) systems in a systematic, experimental design study. Eight significant variables from screening tests in earlier work are tested here. Namely, concentrations of bitumen, silica fines, sodium kaolinite, illite and montmorillonite. In addition, we tested different concentrations of Ca2+, Mg2+, and Na+ in the synthetic process water used with bitumen separation tests. A two level, fractional factorial experimental design allowed testing of the selected variables using only 16 runs. In addition, sodium hydroxide was added as a ninth variable and four repeat tests allowed evaluation of precision. The resulting bitumen recovery model explained 94% of the data variation. The associated parameter estimates were in general agreement with previous experimental observations and with actual operational experience.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".