A numerically based analysis of the sensitivity of conventional and alternative time domain reflectometry probes
Bibliographic record
Abstract
Conventional time domain reflectometry (TDR) probes are comprised of two or three parallel metal rods. Other probes have been designed for water content profiling [Hook et al., 1992; Ferré et al., 1998b; Redman and DeRyck, 1994], surface water content measurement [White and Zegelin, 1992; Selker et al., 1993], or measurement in electrically conductive media. We use the numerical approach of Knight et al. [1997] to predict the responses of variants of these probes when surrounded by materials with different relative dielectric permittivities. These predictions are compared with published calibration curves and analytical solutions where available. Conventional rods are shown to be most sensitive to changes in the water content of the medium. The Hook et al. [1992] probe shows the highest sensitivity of the alternative designs; both surface probes can be used to measure the water content at the soil surface nonintrusively with similar sensitivities. All of the alternative probes have sensitivities that vary with the soil water content, leading to incorrect averaging of the water content if the water content varies along the probes. However, those probes that place nonmetallic components in series with the soil have more pronounced errors than those that place these materials and the soil more nearly in parallel.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".