A new capillary and thin film flow model for predicting the hydraulic conductivity of unsaturated porous media
Bibliographic record
Abstract
Most classical predictive models of unsaturated hydraulic conductivity conceptualize the pore space as either bundles of cylindrical tubes of uniform size or assemblies of cylindrical capillary tubes of various sizes. As such, these models have assumed that liquid configuration is the same in both the wet and dry ranges and that a single concept can be used to describe water transport over the entire range of matric head. Yet theoretical and experimental findings suggest that water transport in wet media, which mostly occurs in water saturated capillaries, is quite different from that in dry media, which occurs in thin liquid films. Following these observations, this paper proposes a new model for predicting the hydraulic conductivity of porous media that accounts for both capillary and thin film flow processes. As with other predictive models, a mathematical relationship is established between hydraulic conductivity and the water retention function. The model is mathematically simple and can easily be integrated into existing numerical models of water transport in unsaturated soils. In sample calculations, the model provided very good agreement with hydraulic conductivity data over the entire range of matric head. Two other well‐supported models, on the other hand, were unable to conform to the experimental data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".