An improved disc infiltrometer method for calculating soil hydraulic properties
Bibliographic record
Abstract
The disc infiltrometer has been established as a field device for in situ measurements of unsaturated infiltration under tensions. Several methods have been developed for calculating hydraulic properties based on infiltration data from the disc infiltrometer. The method by Zhang (1998) has the advantage in using infiltrometer data measured in a relatively short time period to estimate soil hydraulic properties. In this study, we improved this method by replacing the piecewise linear approximation of hydraulic conductivity function (K(h )), which results in overestimated macroscopic capillary length, with the exponential form of K(h). An efficient and accurate iteration procedure was introduced to solve the highly nonlinear equations of the improved method. An experiment was conducted in a large field to measure infiltration processes at various locations with different sizes of disc infiitrometers under different tensions. Based on the infiltration data, the improved method and three other methods in the literature were utilized to estimate soil hydraulic conductivities at different tensions and macroscopic capillary length. Compared with the three methods, the proposed method provided more accurate and stable estimations of the hydraulic conductivity and macroscopic capillary length, using infiltration data collected at short experiment periods within 20 min. Key words: Soil hydraulic conductivity, disc infiltrometer, infiltration rate
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".