Assessment of Soil Moisture Content Measured by Borehole GPR and TDR Under Transient Irrigation and Drainage
Bibliographic record
Abstract
Borehole ground penetrating radar (GPR) was assessed as a method to measure the temporal and spatial variability of soil moisture under uniform wetting and drying conditions on a sandy loam soil. Zero Offset Gather (ZOG) and Multiple Offset Gather (MOG) surveys were conducted before and during the uniform irrigation and the subsequent drainage experiments using the PulseEKKO 100 borehole system with 200MHz antennas in horizontal access tubes. Time domain reflectometry (TDR) data were collected from 10 vertical probes installed at 0.1m increments from 0.1to1.0m below the ground surface. The TDR data were used as standard measures of soil moisture to compare with the GPR estimated soil moisture. Electromagnetic wave velocity along the survey profile at about 1.0m below the ground surface was estimated using ZOG and MOG data by picking the arrival time of the first event. Volumetric soil moisture was calculated using a standard empirical relationship between velocity and water content for each ZOG location. MOG data were used to develop horizontal soil moisture distribution maps at about 1.0m below the soil surface by performing borehole tomogram analyses. Velocity tomograms were developed assuming straight ray paths between the source and receiver antennas and the velocity tomograms were converted to soil moisture maps. High repeatability of soil moisture measurement was found with borehole GPR method since the observed high moisture zones were consistent with time throughout both irrigation and drainage. The radius of influence of the borehole GPR measurements was about 0.5m as determined theoretically and by comparing the GPR and TDR soil moisture contents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".