New Method for Determining Water‐Conducting Macro‐ and Mesoporosity from Tension Infiltrometer
Bibliographic record
Abstract
Characterization of water‐conducting porosity at and near saturation is required in understanding rainfall and snowmelt infiltration and runoff as well as chemical transport in soil. There are methods available to quantify water‐conducting porosity in situ, but with serious limitations. The objective of this paper was to present a general equation for water‐conducting porosity based on ponded‐ and tension‐infiltration measurements. Some analytical solutions are developed for specific unsaturated hydraulic conductivity functions such as the Gardner's exponential and rational power models, Brooks and Corey model, and van Genuchten–Mualem model. Tension infiltrometer measurements were taken at six different pressure heads between −0.3 to −2.2 kPa and double‐ring infiltrometer measurements at a pressure head of 0.35 kPa. The analytical solutions were compared with numerical solutions and existing methods for calculation of water‐conducting porosity. Both the analytical and numerical solutions can reliably determine the water‐conducting porosity of surface soils in situ within the practical pressure head range of the tension infiltrometer. Our method gave consistent water‐conducting porosity, regardless of the width of pressure head ranges. The existing methods overestimated water‐conducting macroporosity by a factor of greater than two and overestimated total water‐conducting porosity by a factor of >10 for measurements taken at large pressure head intervals compared with that of our method. Combining with hydraulic parameter estimation from tension infiltrometer measurements, our method may reduce the number of tension infiltration measurements required to calculate water‐conducting porosity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".