Mixing of Oil in Water Through Electrical Resistance Tomography and Response Surface Methodology
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The mixing performance of the oil‐in‐water dispersion system was evaluated. Using an electrical resistance tomography system composed of two measuring planes, the effect of parameters such as impeller type, impeller speed, oil type, and oil volume fraction on the mixing performance through axial mixing indices were explored. The oil type and the oil volume fraction were identified as the most influential factors on the mixing index. Castor oil, with the highest viscosity of the tested oils, was found as the most difficult oil to disperse. The Scaba impeller was the most efficient impeller in dispersing oil in water. The interactions between oil type and impeller type as well as between impeller speed and oil type, had the greatest impact on the mixing index.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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