Time-domain reflectometry — parametric study for the evaluation of physical properties in soils
Bibliographic record
Abstract
Time-domain reflectometry (TDR) has become a commonly used method in geotechnical engineering to measure the volumetric water content and electrical conductivity in soils. The ability of TDR to accurately determine soil properties depends on the proper understanding of the parameters that affect the propagation of an electromagnetic pulse along the TDR waveguide. The purpose of this paper is to document a parametric study and analyses aimed at gaining a better understanding of TDR measurements and to evaluate the limits in the measurement technique. A parametric study on TDR signals was performed by determining the effects of heterogeneities in the dielectric permittivity, conductivity, and magnetic permeability in sand and gravel specimens. Impedance differences in the probe head were found to contribute to inaccurate travel-time measurements that affect material dielectric permittivity calculations. The calculated relative dielectric permittivity may also be dependent on local changes in porosity near the probes. Tests performed in layered materials indicate that TDR can be used to find abrupt changes in material permittivity, such as the depth to saturation. However, problems in the determination of capillary rise may contribute to uncertainties in the proper determination of permittivity and thicknesses of layers. The presence of ferromagnetic materials was found to change the measured electromagnetic wave velocity. However, the properties of materials outside the radius defined by the probes and beneath the probes minimally affected the TDR results in the two-rod probe used.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".