Mixing Time Analysis Using Colorimetric Methods and Image Processing
Why this work is in the frame
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Bibliographic record
Abstract
A new image analysis technique is proposed to determine the macromixing time in a transparent stirred tank. It consists of capturing on video a decolorization process by using a fast acid−base indicator reaction and employing image analysis to quantify the color evolution. The color change is quantified by means of individual thresholds on the RGB color model and provides a direct measurement of the macromixing evolution as it can be seen by an operator in front of the vessel. It is shown that this technique removes the subjectivity of the estimation of macromixing time by the naked eye, has a high degree of reliability and repeatability, and can yield accurate macromixing information by considering the possible presence of segregated regions and dead zones. Moreover, applications show that the macromixing curves bring new insights to study and compare mixing efficiency of different impellers or multiple impeller mixing systems.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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