Quantitative analysis of water-content estimation errors using ground-penetrating radar data and a low-loss approximation
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Bibliographic record
Abstract
Abstract Expressions are derived to quantify the error when estimating permittivity that results from using the low-loss approximation under lossy conditions and to examine the repercussions on estimating water content θ. Values are computed under a range of porosity, clay-content, water-quality, and frequency conditions. Although in most cases the error is negligible, it can be significant for some hydrogeophysical applications involving cross-hole measurements or low-frequency surface ground-penetrating radar (GPR). For instance, when the loss tangent tanδ equals 0.5, corresponding to an effective conductivity of 30mS/m, a dielectric constantof 11, and a frequency of 100MHz, the relative error on dielectric permittivity is approximately 6%. If the conductivity doubles or the frequency is halved, the loss tangentdoubles but the error grows to 21%. In addition, considering a situation where the porosity is 20% and tanδ=0.5, the use of the low-loss approximation leads to a 10% deviation from θ. In the context of water-content estimation, we therefore suggest to perform attenuation tomography, in addition to velocity tomography for crosshole data, or estimate the quality factor Q for surface GPR data to compute the loss tangent over the probed area. If proven necessary, the parameters sought can then be determined more accurately using a lossy formulation. We also propose to supplement GPR measurements with electrical-resistivity tomography to constrain the borehole GPR amplitude data-processing steps required by attenuation tomography or to complement the characterization of the survey area and improve the knowledge brought by Q estimates alone.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it