Estimation of van Genuchten Equation Parameters in Laboratory and through Inverse Modeling with Hydrus-1D
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Bibliographic record
Abstract
Soil water retention curve (SWRC) becomes important because it guides when and how much to irrigate, optimizing the use of water; can be obtained in the field or laboratory, being commonly determined in the laboratory with porous plate apparatus, and the determination is compromised by issues such as time and labor. In this context, inverse modeling emerges, which allows to obtain a variable going from the effect to the cause, using Hydrus-1D. Hence, this study aims to obtain van Genuchten equation parameters through inverse modeling with Hydrus-1D and make the respective comparisons and inferences. Matric potential data were obtained over time in an instantaneous profile-type experiment. Six sets of three tensiometers each were installed surrounding the center of the experimental plot, at depths of 0.20, 0.35 and 0.50 m. Target depth was 0.35 m, where the roots of most crops are concentrated, and the other tensiometers were used to obtain the potential gradient. Matric potential data were used to feed Hydrus-1D and obtain the van Genuchten equation parameters. Laboratory curves were obtained using porous plate apparatus, with four replicates. It was concluded that, in general, the Hydrus-1D model estimates van Genuchten equation parameters and, consequently, the SWCC of an Argissolo more consistently with field conditions than those obtained in the laboratory; and, provided it is fed with field data, the Hydrus-1D simulates well the behavior of matric potential and moisture over time, reducing the time and labor in the procedures to obtain van Genuchten equation parameters in the laboratory.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it