Thermodiffusion effect for a non‐associating mixture in a multi layered system of porous media and fluid layers heated from above
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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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".