An extension of the capillary and thin film flow model for predicting the hydraulic conductivity of air‐free frozen porous media
Bibliographic record
Abstract
Hydraulic conductivity of frozen air‐free porous media is a rather elusive property that remains largely undefined in much of the literature. According to modern science, water transport in frozen porous media occurs mostly in ice‐free capillaries at temperatures close to the freezing point of pure water and through a thin liquid interlayer, between solid particle and ice, at lower temperatures. In accordance with this understanding, this paper extends the capabilities of an existing capillary and thin film flow model to include the prediction of hydraulic conductivity in frozen air‐free porous media. As such, hydraulic conductivity of the frozen porous media is predicted with a simple capillary bundle model as well as with a new hydrodynamic model of thin interlayer flow in which film thickness is controlled by both London‐van der Waals and ionic‐electrostatic forces. As with other predictive models of hydraulic conductivity, most model parameters are derived from more easily measured water content data in either ice‐free (water retention function) or air‐free (water freezing function) porous media. Model results showed very good agreement with observed values of hydraulic conductivity taken at thermal equilibrium, and illustrated the importance of thin interlayer flow at lower temperatures.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".