Thermodiffusion effect for a non‐associating mixture in a multi layered system of porous media and fluid layers heated from above
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Bibliographic record
Abstract
Abstract A numerical simulation has been conducted to investigate the thermodiffusion phenomenon in a porous layer sandwiched between two liquid layers that are wetted with water alcohol mixtures at different water concentrations. Two different binary mixtures with different Soret coefficients have been used in the entire system, one with a negative Soret coefficient and the other one with a positive sign. The results show that the direction of the component migration in a porous layer depends on the sign of the Soret coefficient. For a binary mixture with a negative Soret coefficient, such as 10% isopropanol and 90% water, the heavier species move in the direction of the hot surface, while for a mixture with a positive Soret effect, such as 50% isopropanol and 50% water, the heavier species migrate toward the colder surface. To reduce the gravity effect, the cavity was heated from the top with different temperatures ranging from 5 to 20 K.
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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 | 0.000 |
| 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) | 0.000 | 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