Magnetic Resonance Monitoring and Numerical Modeling of Soil Moisture during Evaporation
Bibliographic record
Abstract
Core Ideas Evaporative drying of soil columns was monitored in high resolution using NMR. Soil moisture data were used for validating a water, vapor, and heat flow model. The Richards approach was insufficient under Stage II conditions. NMR is a feasible technique for soil moisture measurements under Stage II conditions. Evaporation from bare soil surfaces can be restrained to a great extent with the development of a dry layer at the soil surface where capillary hydraulic conductance ceases and water flow proceeds only by gas phase transport. Model calculations and preliminary experiments with model porous media have shown that this surface layer can be very thin. An accurate characterization of these processes is required, which is provided by noninvasive magnetic resonance (MR) methods. The evaporative drying of a silt loam and a sandy loam was monitored at high spatial resolution in laboratory experiments. The MR data were used to assess the performance of two numerical models: (i) the Richards equation, which considers isothermal liquid water flow, and (ii) a coupled soil water, heat, and vapor flow numerical model. The experimental results reveal two distinct drying regimes for both soil types where, at the onset of the second evaporation stage, a dry surface zone developed with increasing thickness over time. This layer revealed that water loss inside the soil coincided with a relatively low evaporation rate as the liquid continuity to the soil surface vanished. The modeling results clearly demonstrated the need to consider heat and vapor flow. It was shown, as a proof of principle, that MR relaxation time spectra may serve as a proxy to follow desaturation processes where spatially resolved transverse relaxation can reveal a secondary evaporation front.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".