Comparison of Petrophysical Relationships for Soil Moisture Estimation using GPR Ground Waves
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Bibliographic record
Abstract
Soil water content measurement using ground‐penetrating radar (GPR) requires an appropriate petrophysical relationship between the dielectric permittivity and volumetric water content of the soil. The suitability of different relationships for GPR soil water content estimation has not been thoroughly investigated under natural field conditions for a complete range of seasonal soil conditions. In this study, we examined the ability of various empirical relationships, volumetric mixing formulae, and effective medium approximations to predict near‐surface volumetric soil water content using high‐frequency direct ground wave (DGW) velocity measurements for three soil textures. The estimated water contents were compared with values obtained from gravimetric sampling. The accuracy of soil water content predictions obtained from the various relationships ranged considerably. The best predictions for the overall data set in terms of RMSE were obtained with a differential effective medium approximation based on a coated sphere model (RMSE = 0.045 m 3 m −3 ); however, an empirical relationship (RMSE = 0.052 m 3 m −3 ) and a volumetric mixing formula (RMSE = 0.048 m 3 m −3 ) also performed well. These best‐fitting relationships do exhibit some degree of textural bias that should be considered in the choice of petrophysical relationship for a given data set. Further improvements in water content estimates were obtained using our best‐fit third‐order polynomial relationship (RMSE = 0.041 m 3 m −3 ) and our three‐phase volumetric mixing formula with geometric parameter α = 0.36 (RMSE = 0.042 m 3 m −3 ); these optimized relationships were developed using the DGW permittivity and soil water content data collected in this study.
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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