Measuring Hydraulic Properties Using a Line Source II. Field Test
Bibliographic record
Abstract
We designed and tested a field method to measure unsaturated soil hydraulic properties using multi‐purpose time domain reflectometry (TDR) probes below a surface line source with constant flux of water. The surface line source was produced with a moving irrigation system at a field site sheltered from precipitation. Two hundred multi‐purpose TDR probes were vertically installed in the soil beneath the line source to measure soil water pressure head (ψ), water storage ( W ), and tracer travel time ( T ). The soil hydraulic properties, the inverse macroscopic capillary length (α), hydraulic conductivity at saturation ( K s ), and soil water content at saturation (θ s ) were estimated by inverse procedures with new analytical expressions. Five combinations of measurement sets, namely W ‐only, ψ and W , ψ and T , and W and T , and ψ and W and T were used. Approximate confidence contours in the α– K s plane were calculated to show the precision of the parameter estimates. For comparison, hydraulic properties were also measured by means of the Guelph Permeameter (GP) and the modified Guelph Pressure Infiltrometer (GPI) systems. Hydraulic parameters estimated from only W measurements were similar to those estimated from the combinations of W and ψ, or W and T , or W and ψ and T The estimated hydraulic parameters were similar to those obtained with three‐dimensional (3‐D) infiltration measurements by means of the GP and GPI systems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".