An examination of direct ground wave soil moisture monitoring over an annual cycle of soil conditions
Bibliographic record
Abstract
Direct ground wave (DGW) measurements obtained with ground‐penetrating radar have been used in a number of previous studies to estimate volumetric water content in the shallow soil zone; however, these studies have generally involved controlled field experiments or measurements collected across limited natural ranges of soil moisture conditions. To further investigate the capacity of this method, we have undertaken an extensive field study using multifrequency (i.e., 225, 450, and 900 MHz) DGW measurements to monitor a complete annual cycle of soil water content variations typical of midlatitude climates at three sites with different soil textures. The use of common‐midpoint surveys allowed us to understand the nature and evolution of the near‐surface electromagnetic wavefields and their impact on DGW moisture predictions. We present the novel characterization of a wide‐range of seasonal moisture dynamics including soil freezing and thawing process using multifrequency DGW measurements for a range of soil textures. These data showed significant temporal variations in both the near‐surface wavefield and multifrequency DGW velocities corresponding to both seasonal and shorter‐term variations in soil conditions. Although all of the measurement sites displayed similar temporal responses, the rate and magnitude of these velocity variations corresponded to varying soil water contents, which were controlled by the soil textural properties. Although there were no observed systematic differences in DGW velocities due to frequency dispersion for the 225–900 MHz range, the DGW measurements obtained using higher‐frequency antennas was less impacted by near‐surface wavefield interference due to their shorter signal pulse duration. DGW velocity measurements combined with an appropriate dielectric mixing formula provided quantitative predictions of soil water content that accurately replicated the soil sample data over the annual cycle of moisture conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".