Evaluation of Uncoated and Coated Time Domain Reflectometry Probes for High Electrical Conductivity Systems
Bibliographic record
Abstract
High sample electrical conductivity reduces the quality of a time domain reflectrometry (TDR) waveform by the loss of signal amplitude. Two strategies are examined to obtain higher signal/noise waveforms: (i) waveform differencing by remote diode shorting and (ii) covering probe conductors with resistive coatings. Experiments using electrically conductive water solutions (0–5 dS m −1 ) and three‐rod Zegelin type probes show conventional dual‐tangent waveform analysis and waveform differencing using manual short circuits can accurately determine travel time but the remote diode shorting method can be systematically biased by the electrical properties of the diodes used. Three‐rod Zegelin‐type probes with a high resistance coating on the central rod permit collection of analyzable waveforms for solutions with electrical conductivities at least as high as 70 dS m −1 Dual‐tangent analysis of the raw waveform is found to be more accurate than the remote diode shorting method within water solutions and within silica sand saturated with an electrically conductive water solution. The probe coating creates a nonlinear relationship between the apparent dielectric permittivity estimated using a coated probe and the actual sample apparent dielectric permittivity. Experimental measurements of this relationship can be fitted using an equation of the form for a coaxial cell. A three‐rod coated probe with a single diode at the probe head is a practical means to collect interpretable waveforms in media with high electrical conductivity. However, measurements of travel time alone may not be sufficient to determine water content in soils with high concentrations of dissolved ions in the soil water solution.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".