A Low-Cost Automated Test Column to Estimate Soil Hydraulic Characteristics in Unsaturated Porous Media
Bibliographic record
Abstract
The estimation of soil hydraulic properties in the vadose zone has some issues, such as accuracy, acquisition time, and cost. In this study, an inexpensive automated test column (ATC) was developed to characterize water flow in a homogeneous unsaturated porous medium by the simultaneous estimation of three hydraulic state variables: water content, matric potential, and water flow rates. The ATC includes five electrical resistance probes, two minitensiometers, and a drop counter, which were tested with infiltration tests using the Hydrus-1D model. The results show that calibrations of electrical resistance probes reasonably match with similar studies, and the maximum error of calibration of the tensiometers was 4.6% with respect to the full range. Data measured by the drop counter installed in the ATC exhibited a high consistency with the electrical resistance probes, which provides an independent verification of the model and indicates an evaluation of the water mass balance. The study results show good performance of the model against the infiltration tests, which suggests a robustness of the methodology developed in this study. An extension to the applicability of this system could be successfully used in low-budget projects in large-scale field experiments, which may be correlated with resistivity changes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".