Calibration and modification of impedance probe for near surface soil moisture measurements
Bibliographic record
Abstract
A reliable and low cost sensor that can measure soil moisture at or near the soil surface is currently not available. The objectives of this study were: (i) to evaluate the possibility of modifying an impedance probe (IP) to measure soil moisture content at a very shallow depth (2–5 cm); and (ii) to compare the soil moisture values obtained using the IP to the values obtained using the traditional gravimetric method. The research was conducted at the Winfred A. Thomas Agricultural Research Station (WTARS) Hazel Green, Alabama. The standard IP that is capable of measuring soil moisture content at 6-cm soil depth was modified to measure soil moisture at 2-, 3-, and 5-cm depths. Using a site and depth-specific calibration technique it provided results that were comparable to the values that were obtained following the traditional gravimetric water content determination protocol. We found that the instrument was very sensitive to changes in soil moisture content and has great potential as a replacement for the gravimetric technique. It allows repetitive measurements of soil moisture content at a very shallow depth with minimal soil disturbance. Fur thermore, the instrument is particularly valuable for providing ground- truth soil moisture contents to validate remotely sensed data. Key words: Soil moisture, remote sensing, impedance probe, ground-truth, validation
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".