Measuring Hydraulic Properties Using a Line Source I. Analytical Expressions
Bibliographic record
Abstract
In situ measurement of soil hydraulic properties remains a challenge. This study presents new analytical expressions for estimation of soil hydraulic properties below a surface line source by means of multi‐purpose time domain reflectometry (TDR) probes and existing quasi‐analytical, steady‐state solutions for infiltration from a surface line source. Inverse procedures are used to estimate the inverse macroscopic capillary length scale, α, and the hydraulic conductivity at saturation, K s , from pressure head (ψ), water storage ( W ), and conservative ionic tracer travel time ( T ) measured via multi‐purpose TDR probes placed at several depths below a line source with constant flux of water. Soil water content at saturation, θ s , can also be estimated if prior information is available. The parameter and spatial sensitivities of each solution were calculated by means of sensitivity coefficients. The uniqueness of possible combinations of measurements to estimate α, K s , and θ s was tested by means of two‐dimensional response surfaces. Significant correlation exists between K s and θ s , and thus it is not possible to estimate both K s and θ s by globally minimizing the objective function. Combination approaches with W (i.e., ψ and W , T and W , or ψ and W and T ) give unique estimates of α and K s if either θ s is known or prior information on θ s is available.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".