Surfactant‐Induced Unsaturated Flow: Instrumented Horizontal Flow Experiment and Hysteretic Modeling
Bibliographic record
Abstract
Organic compounds with surface‐active properties reduce the surface tension of water and can change soil water contact angles depending on their aqueous concentration. The presence of such compounds in the unsaturated zone affects soil moisture characteristics and unsaturated hydraulic conductivity relations, and can cause flow as a result of induced soil water pressure gradients. Some studies have used horizontal column experiments to measure the surfactant‐driven movement of water in unsaturated media; however, they have all used destructive sampling methods to determine water contents and did not measure soil water pressures. We attempted to gain better insight into this unsaturated soil water flow process through continual monitoring of water contents with time domain reflectometry and soil water pressures with pressure transducer equipped tensiometers. One half of the horizontal one‐dimensional flow cell was prewetted (but unsaturated) with water while the other half contained the same fluid content of 7% butanol solution. The temporal and spatial water content and soil water pressure information improved our understanding of the surfactant‐induced flow, including perturbations associated with solute gradients. Backflow in the flow cell was observed at later times as the water‐content‐induced component of the hydraulic gradients became more important. Hysteretic numerical simulations of the one‐dimensional horizontal flow cell using a modified version of HYDRUS 5.0 including coupled flow and transport through concentration‐dependent surface tensions were performed for the case in which butanol is the surface‐active compound. The numerical simulations, which used independently measured flow and transport parameters, provided a good fit to the experimental data and provided further insight into the induced flow 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 | 0.000 |
| 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 | 0.001 | 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".