Determination of Hydraulic Properties in Sloping Landscapes from Tension and Double-Ring Infiltrometers
Bibliographic record
Abstract
The majority of landscapes, natural or cultivated, are nonlevel. However, specifically designed instruments are not available for estimation of soil hydraulic properties in sloping landscapes. The objective of this study is to examine if tension and double-ring infiltrometers are suitable for determination of soil hydraulic properties on sloping soil surfaces. A field experiment was conducted in a silt loam soil (Typic Haplustolls) in Saskatchewan, Canada to explore the usefulness of tension and double-ring infiltrometers for the determination of soil hydraulic properties in sloping landscapes. Soil surfaces were created to represent four treatments, 0 (level), 7, 15, and 20% slopes. For each treatment, water infiltration rates were measured using a double-ring infiltrometer and a tension infiltrometer at −3, −6, −10, −13, −17, and −22 cm water pressure heads. In addition, three-dimensional computer simulation studies were performed for a tension infiltrometer with various disc diameters and water pressure heads for different surface slopes. Steady-state infiltration rate, field-saturated hydraulic conductivity, unsaturated hydraulic conductivity as a function of water pressure head, macroscopic capillary length parameter, and water-conducting macro- and mesoporosity were compared for different surface slopes. These parameters were not significantly different (p < 0.05) between level and sloping lands. Experimental and numerical results of this study suggest that both tension and double-ring infiltrometers are suitable for characterization of surface soil hydraulic properties in landscapes with slopes up to 20%.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".