A sorption isotherm model for soil incorporating external and internal surface adsorption, and capillarity
Bibliographic record
Abstract
Soil water sorption isotherm is governed by soil–water interactions of adsorption and capillarity, depending on particle surface properties and pore size distribution, respectively. Recent studies have separated soil particle surfaces into two categories in terms of distinct adsorption behaviors, i.e., external and internal surfaces, identifying three independent sorption processes with varying energy levels, namely external adsorption, internal adsorption, and capillarity. Here, an isotherm model is developed to describe water sorption on soils in the full relative humidity ( RH) range by incorporating the three physical processes. The internal and external adsorptions are represented by an augmented-Brunauer–Emmett–Teller (A-BET) equation, and capillarity is described by Kosugi’s model. Two scaling factors are introduced to expand A-BET equation’s application RH range from 0%−40% to 0%−100%—one for the reduction of available adsorption area in restricted pores and another for the disparity between heats of adsorption and liquefaction. Experimental validation demonstrates that the proposed model excellently matches the isotherm data for a wide array of soils in the full RH range. Further, the proposed model performs well in capturing soil water retention data in the full matric potential range, providing a seamless linkage between soil sorption isotherm and water retention behaviors.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".