Quantitative analysis of water-content estimation errors using ground-penetrating radar data and a low-loss approximation
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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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".