Measuring Soil Hydraulic Properties Using a Cased Borehole Permeameter: Falling‐Head Analysis
Bibliographic record
Abstract
The falling‐head cased borehole analysis of Philip for determining the field‐saturated hydraulic conductivity ( K fs ) and sorptive number (α*) in the vadose zone was extended and assessed. The extension allows several water discharge configurations (vertical, radial, combined vertical and radial), variable length ( L ) and radius ( a ) of outflow screen, variable reservoir radius, and use of Excel Solver to calculate K fs and α* via numerical optimization. The assessment used simulated borehole drawdown curves from HYDRUS‐2D to determine the accuracy with which the Philip and extended solutions determined K fs and α*. Using the Philip “flow efficiency correction,” C P = π 2 /8, and “gravity factor,” G P = equivalent sphere radius r 0 (maximum gravity effect), caused the Philip and extended solutions to overestimate K fs or α* by amounts ranging from a few percent in strong‐capillarity soils to several orders of magnitude in weak‐capillarity soils. Replacing C P with C E = 1 (no flow efficiency correction) and G P with G E = 0 (negligible gravity effect) in the extended solution resulted in accurate K fs determinations (≤20% error) regardless of discharge geometry, screen configuration, drawdown range, or soil capillarity. Consistently accurate determination of α* required use of the C E and G E coefficients, 1 ≤ L / a ≤ 4, and avoidance of small borehole drawdowns. Solver‐based determination of K fs and α* using several heads distributed along the drawdown curve was less sensitive to measurement error and lack of data‐model fit than the two‐head approaches of Philip and others. The extended analysis improves the accuracy and utility of the falling‐head cased borehole permeameter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".